diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 8b19aee6..cff06570 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -30,9 +30,6 @@ jobs:
           extra-specs: |
             pytest
 
-      - name: Install MM
-        run: python -m pip install -e . --no-deps -v
-
       - if: ${{ matrix.monet == 'dev' }}
         name: Install development versions of monet and monetio
         run: |
@@ -49,7 +46,7 @@ jobs:
           python -c "import monet;   print('monet.__version__',   getattr(monet, '__version__', '?'))"
 
       - name: pytest
-        run: python -m pytest melodies_monet
+        run: python -m pytest -v melodies_monet
       
       - name: Run docs/examples notebooks as scripts
         run: |
@@ -62,12 +59,33 @@ jobs:
           done
           cd -
 
+      - name: Prepare idealized save/read cases
+        shell: python
+        run: |
+          from copy import deepcopy
+          import yaml
+
+          with open('docs/examples/control_idealized.yaml') as f:
+            ctl = yaml.safe_load(f)
+          assert {'save', 'read'} < ctl['analysis'].keys()
+
+          ctl_save = deepcopy(ctl)
+          del ctl_save['analysis']['read']
+          with open('docs/examples/control_idealized_save.yaml', 'w') as f:
+            yaml.safe_dump(ctl_save, f)
+
+          ctl_read = deepcopy(ctl)
+          del ctl_read['analysis']['save']
+          with open('docs/examples/control_idealized_read.yaml', 'w') as f:
+            yaml.safe_dump(ctl_read, f)
+
       - name: Check CLI works
         run: |
           cd docs/examples
           melodies-monet --version
           python -m melodies_monet --version
-          melodies-monet run control_idealized.yaml
+          melodies-monet run control_idealized_save.yaml
+          melodies-monet run control_idealized_read.yaml
           cd -
 
   docs:
@@ -81,7 +99,7 @@ jobs:
       - uses: actions/checkout@v3
 
       - name: Set up Python (micromamba)
-        uses: mamba-org/provision-with-micromamba@v12
+        uses: mamba-org/provision-with-micromamba@v15
         with:
           environment-file: docs/environment-docs.yml
           cache-env: true
diff --git a/docs/appendix/yaml.rst b/docs/appendix/yaml.rst
index 11febcc4..5c18494f 100644
--- a/docs/appendix/yaml.rst
+++ b/docs/appendix/yaml.rst
@@ -106,6 +106,10 @@ data (e.g., surf_only: True).
 Typically this is set at the horizontal resolution of your model * 1.5. Setting 
 this to a smaller value will speed up the pairing process. 
 
+**apply_ak:** This is an optional argument used for pairing of satellite data. This
+should be set to True when application of satellite averaging kernels or apriori data 
+to model observations is desired. 
+
 **mapping:** This is the mapping dictionary for all variables to be plotted. 
 For each observational dataset, add a mapping dictionary where the model 
 variable name is first (i.e., key) and the observation variable name is second 
@@ -362,8 +366,8 @@ observation label is first and the model label is second
      used for averaging and plotting. Options are 'time' for UTC or 'time_local' 
      for local time
    * **ts_avg_window:** This is for timeseries plots only. This is the averaging 
-     window applied to the data. Options are None for no averaging or a pandas 
-     resample rule (e.g., 'H' is hourly, 'D' is daily).
+     window applied to the data. No averaging done if not provided in the yaml file (i.e., ts_avg_window is optional). Averaging is done if a pandas 
+     resample rule (e.g., 'H' is hourly, 'D' is daily) is specified.
    
 Stats
 -----
diff --git a/docs/background/gridded_datasets.rst b/docs/background/gridded_datasets.rst
new file mode 100644
index 00000000..27913aac
--- /dev/null
+++ b/docs/background/gridded_datasets.rst
@@ -0,0 +1,5 @@
+Gridded Datasets
+================
+
+Gridded datasets
+
diff --git a/docs/background/time_chunking.rst b/docs/background/time_chunking.rst
new file mode 100644
index 00000000..0d0e9b0a
--- /dev/null
+++ b/docs/background/time_chunking.rst
@@ -0,0 +1,5 @@
+Time Chunking
+================
+
+Time chunking
+
diff --git a/docs/conf.py b/docs/conf.py
index 6f7cced6..4c56af0e 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -199,6 +199,7 @@
     # Sphinx 4.5 linkcheck having problem:
     "https://docs.github.com/en/authentication/connecting-to-github-with-ssh/adding-a-new-ssh-key-to-your-github-account",
 ]
+user_agent = "Mozilla/5.0 (X11; Linux x86_64; rv:25.0) Gecko/20100101 Firefox/25.0"
 
 autosectionlabel_prefix_document = True
 autosectionlabel_maxdepth = 2
diff --git a/docs/develop/developers_guide.rst b/docs/develop/developers_guide.rst
index 9f1e3366..f75a0637 100644
--- a/docs/develop/developers_guide.rst
+++ b/docs/develop/developers_guide.rst
@@ -74,7 +74,7 @@ these instructions:
 
        $ conda create --name melodies-monet python=3.9
        $ conda activate melodies-monet
-       $ conda install -y -c conda-forge pyyaml monet monetio netcdf4 wrf-python typer rich pooch jupyterlab
+       $ conda install -y -c conda-forge pyyaml pandas=1 monet monetio netcdf4 wrf-python typer rich pooch jupyterlab
 
 (b) Clone [#clone]_ and link the latest development versions of MONET and MONETIO from GitHub to
     your conda environment::
diff --git a/docs/environment-docs.yml b/docs/environment-docs.yml
index f8dec82d..7fa4cbce 100644
--- a/docs/environment-docs.yml
+++ b/docs/environment-docs.yml
@@ -12,6 +12,7 @@ dependencies:
   - netcdf4
   - numpy
   - pandas<2
+  - pillow<10
   #
   # Extras
   - pooch
diff --git a/docs/index.rst b/docs/index.rst
index 8fe2972f..0bb6a0c0 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -47,6 +47,8 @@ MONETIO please refer to:
    background/supported_analyses
    background/supported_plots
    background/supported_stats
+   background/time_chunking
+   background/gridded_datasets
    
 .. toctree::
    :hidden:
diff --git a/docs/tutorial/installation.rst b/docs/tutorial/installation.rst
index 35b7f3dd..2c9c7ccf 100644
--- a/docs/tutorial/installation.rst
+++ b/docs/tutorial/installation.rst
@@ -34,7 +34,7 @@ First create and activate a conda environment::
 
 Add dependencies from conda-forge::
 
-    $ conda install -y -c conda-forge pyyaml monet monetio netcdf4 wrf-python typer rich pooch
+    $ conda install -y -c conda-forge pyyaml pandas=1 monet monetio netcdf4 wrf-python typer rich pooch
 
 Now, install the stable branch of MELODIES MONET to the environment::
 
diff --git a/examples/jupyter_notebooks/MM-example-mopitt.ipynb b/examples/jupyter_notebooks/MM-example-mopitt.ipynb
new file mode 100644
index 00000000..8c5038af
--- /dev/null
+++ b/examples/jupyter_notebooks/MM-example-mopitt.ipynb
@@ -0,0 +1,636 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5d7309d7",
+   "metadata": {},
+   "source": [
+    "# MELODIES-MONET example with MOPITT CO\n",
+    "\n",
+    "First lets just import the driver"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "67a852a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Please install s3fs if retrieving from the Amazon S3 Servers.  Otherwise continue with local data\n",
+      "Please install h5netcdf to open files from the Amazon S3 servers.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../../')\n",
+    "import driver"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ce382fa",
+   "metadata": {},
+   "source": [
+    "### Driver class\n",
+    "\n",
+    "First, we initialize the python driver analysis class. It consists of 3 main components/processes; 1. model instances, 2. observation instances, 3. a paired instance of both.  This helps us set up the comparisons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cc06d0f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "an = driver.analysis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e64adc7",
+   "metadata": {},
+   "source": [
+    "### Control File\n",
+    "\n",
+    "Read in all the comparison definitions from the yaml control file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "52285916",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "an.control = '../yaml/control_mopitt.yaml'\n",
+    "an.read_control()\n",
+    "#an.control_dict\n",
+    "#an.control_dict['obs']['mopitt_l3']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11454f7f",
+   "metadata": {},
+   "source": [
+    "### Open Obs\n",
+    "\n",
+    "Load all the data files. Satellites data is usually hdf or netCDF, although sometimes are saved as ascii or other unusual formats. Note the data needs to be already accessible on the system you are working on. Future functionality will include OpenDap. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5bb5f935",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading MOPITT\n",
+      "/glade/work/buchholz/data/MOPITT/MOP03JM-202008-L3V95.9.3.he5\n",
+      "/glade/work/buchholz/data/MOPITT/MOP03JM-202009-L3V95.9.3.he5\n",
+      "/glade/work/buchholz/data/MOPITT/MOP03JM-202010-L3V95.9.3.he5\n"
+     ]
+    }
+   ],
+   "source": [
+    "an.open_obs()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a11be74-a756-4757-8494-c75d6e0906ad",
+   "metadata": {},
+   "source": [
+    "We can look at the data we just loaded based on the observation names defined in the yaml dictionary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "344c3f8e",
+   "metadata": {},
+   "outputs": [
+    {
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+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:  (time: 3, lon: 360, lat: 180)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 2020-08-01T00:00:11.125999872 ... 2020-10-...\n",
+       "  * lon      (lon) float32 -179.5 -178.5 -177.5 -176.5 ... 177.5 178.5 179.5\n",
+       "  * lat      (lat) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
+       "Data variables:\n",
+       "    column   (time, lon, lat) float32 nan nan nan nan nan ... nan nan nan nan</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6b6bfd62-fd44-4ff4-ac2d-66cc1a4fc7fd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6b6bfd62-fd44-4ff4-ac2d-66cc1a4fc7fd' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3</li><li><span class='xr-has-index'>lon</span>: 360</li><li><span class='xr-has-index'>lat</span>: 180</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-efe965ab-f64b-4dac-bcf6-23708197a876' class='xr-section-summary-in' type='checkbox'  checked><label for='section-efe965ab-f64b-4dac-bcf6-23708197a876' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-01T00:00:11.125999872 .....</div><input id='attrs-af8edd5e-da1c-4bb3-bb46-271c56adae52' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af8edd5e-da1c-4bb3-bb46-271c56adae52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b35f3eec-987b-4fbc-a709-0d85f73fa6ea' class='xr-var-data-in' type='checkbox'><label for='data-b35f3eec-987b-4fbc-a709-0d85f73fa6ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-01T00:00:11.125999872&#x27;, &#x27;2020-09-01T00:00:23.621999872&#x27;,\n",
+       "       &#x27;2020-10-01T00:00:16.392999680&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.5 -178.5 ... 178.5 179.5</div><input id='attrs-11600d2d-2cd4-4467-b6f9-11f4f1987407' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-11600d2d-2cd4-4467-b6f9-11f4f1987407' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-603989d1-4d6d-43de-95e0-ad9256394839' class='xr-var-data-in' type='checkbox'><label for='data-603989d1-4d6d-43de-95e0-ad9256394839' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-4507f251-5f07-459f-b8a0-be5a750ca388' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4507f251-5f07-459f-b8a0-be5a750ca388' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a5918c2-ca55-4df0-99dc-24825bfbd754' class='xr-var-data-in' type='checkbox'><label for='data-9a5918c2-ca55-4df0-99dc-24825bfbd754' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
+       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
+       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
+       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
+       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
+       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
+       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
+       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
+       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
+       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
+       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
+       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
+       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
+       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
+       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
+       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
+       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
+       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
+       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-122e5b72-9575-4d56-acf2-bb43e73e48cc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-122e5b72-9575-4d56-acf2-bb43e73e48cc' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>column</span></div><div class='xr-var-dims'>(time, lon, lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-bc5f8907-f270-4749-928c-e8744f8c6ac7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc5f8907-f270-4749-928c-e8744f8c6ac7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64179614-8856-4366-a2e9-b5d753e7e4df' class='xr-var-data-in' type='checkbox'><label for='data-64179614-8856-4366-a2e9-b5d753e7e4df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4eba620e-e393-4e48-b519-857dec0c0b71' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4eba620e-e393-4e48-b519-857dec0c0b71' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (time: 3, lon: 360, lat: 180)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 2020-08-01T00:00:11.125999872 ... 2020-10-...\n",
+       "  * lon      (lon) float32 -179.5 -178.5 -177.5 -176.5 ... 177.5 178.5 179.5\n",
+       "  * lat      (lat) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
+       "Data variables:\n",
+       "    column   (time, lon, lat) float32 nan nan nan nan nan ... nan nan nan nan"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#All the info in the observation class can also be called.\n",
+    "an.obs['mopitt_l3'].obj\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81f4e70c-f074-4c6d-a350-9f080042b4cb",
+   "metadata": {},
+   "source": [
+    "### Test plotting\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "309f3ea5-1aca-4ec3-82ef-c39116fbf3e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import cartopy.crs as ccrs                 # For plotting maps\n",
+    "import cartopy.feature as cfeature         # For plotting maps\n",
+    "from cartopy.util import add_cyclic_point  # For plotting maps\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c2d7d39f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,10))\n",
+    "\n",
+    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
+    "ax.add_feature(cfeature.COASTLINE)\n",
+    "\n",
+    "clev = np.arange(0.5*1e18, 3.8*1e18, 0.25*1e18)\n",
+    "plt.contourf(an.obs['mopitt_l3'].obj.lon,an.obs['mopitt_l3'].obj.lat,an.obs['mopitt_l3'].obj.column[0,:,:].transpose(), clev, cmap='Spectral_r',extend='both')\n",
+    "\n",
+    "cbar = plt.colorbar(shrink=0.6)\n",
+    "cbar.ax.tick_params(labelsize=20) \n",
+    "\n",
+    "#plt.show()\n",
+    "plt.savefig('mopitt_example.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "534e0424-cdbd-4084-96d3-36328521bee0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:miniconda3-monet_py39]",
+   "language": "python",
+   "name": "conda-env-miniconda3-monet_py39-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/jupyter_notebooks/MM_trp_no2_l2_func.ipynb b/examples/jupyter_notebooks/MM_trp_no2_l2_func.ipynb
new file mode 100644
index 00000000..38306541
--- /dev/null
+++ b/examples/jupyter_notebooks/MM_trp_no2_l2_func.ipynb
@@ -0,0 +1,199 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1c1a4942",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "502b2359",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sys.path.append('../../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4fe2d1ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Please install s3fs if retrieving from the Amazon S3 Servers.  Otherwise continue with local data\n",
+      "Please install h5py to open files from the Amazon S3 servers.\n",
+      "Please install h5netcdf to open files from the Amazon S3 servers.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import driver"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7373a0dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "an = driver.analysis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9b60c80f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading TROPOMI L2 NO2\n",
+      "/Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/*\n",
+      "reading /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/S5P_OFFL_L2__NO2____20220430T222541_20220501T000711_23559_02_020301_20220502T140952.nc\n",
+      "qa_value\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "lon\n",
+      "DEBUG:root:lon\n",
+      "lat\n",
+      "DEBUG:root:lat\n",
+      "qa_value\n",
+      "nitrogendioxide_tropospheric_column\n",
+      "DEBUG:root:nitrogendioxide_tropospheric_column\n"
+     ]
+    }
+   ],
+   "source": [
+    "an.control = '../yaml/control_tropomi_l2_no2.yaml'\n",
+    "an.read_control()\n",
+    "an.open_obs()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1b065328",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OrderedDict([('20220430',\n",
+       "              <xarray.Dataset>\n",
+       "              Dimensions:                              (dim_0: 4173, dim_1: 450)\n",
+       "              Dimensions without coordinates: dim_0, dim_1\n",
+       "              Data variables:\n",
+       "                  lon                                  (dim_0, dim_1) float32 nan nan ... nan\n",
+       "                  lat                                  (dim_0, dim_1) float32 nan nan ... nan\n",
+       "                  qa_value                             (dim_0, dim_1) float32 0.0 0.0 ... 0.0\n",
+       "                  nitrogendioxide_tropospheric_column  (dim_0, dim_1) float32 nan nan ... nan\n",
+       "              Attributes:\n",
+       "                  quality_flag:        qa_value\n",
+       "                  quality_thresh_min:  0.7)])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "an.obs['tropomi_l2_no2'].obj"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "28adfe97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "from cartopy.util import add_cyclic_point\n",
+    "import numpy as np\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "74112ff5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:matplotlib.colorbar:locator: <matplotlib.ticker.FixedLocator object at 0x7ff68d7943d0>\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lon = an.obs['tropomi_l2_no2'].obj['20220430']['lon']\n",
+    "lat = an.obs['tropomi_l2_no2'].obj['20220430']['lat']\n",
+    "no2 = an.obs['tropomi_l2_no2'].obj['20220430']['nitrogendioxide_tropospheric_column']\n",
+    "\n",
+    "plt.figure(figsize=(20,10))\n",
+    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
+    "clev = np.arange(1*1e15, 5.0*1e15, 0.25*1e15)\n",
+    "plt.contourf(lon, lat, no2, clev, cmap='Spectral_r', extend='both')\n",
+    "cbar=plt.colorbar(shrink=0.6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "071c35f8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/jupyter_notebooks/Monet-example-omps_nm_raqms-time_chunk.ipynb b/examples/jupyter_notebooks/Monet-example-omps_nm_raqms-time_chunk.ipynb
new file mode 100644
index 00000000..1d049578
--- /dev/null
+++ b/examples/jupyter_notebooks/Monet-example-omps_nm_raqms-time_chunk.ipynb
@@ -0,0 +1,125 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "51def9a8-dcf7-489c-a9e6-bc78886a7917",
+   "metadata": {},
+   "source": [
+    "## Implementation of processing over time intervals\n",
+    "\n",
+    "Testing of Melodies-Monet OMPS Nadir Mapper L2 pairing with time interval chunking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64e61a73-ace6-4041-a194-fe26e7a6b126",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from melodies_monet import driver"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17e4ebe7-7b5e-4628-919d-7275dd088478",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "an = driver.analysis()\n",
+    "an.control = '../yaml/control_omps_nm-raqms.yaml'\n",
+    "an.read_control()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2bc2dc2d-5297-4827-aa3e-90052ea4b64c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "base_prefix = an.save['paired']['prefix']#.copy()\n",
+    "for t in an.time_intervals:\n",
+    "    an.open_models(time_interval=t)\n",
+    "    an.open_obs(time_interval=t)\n",
+    "    \n",
+    "    an.pair_data()\n",
+    "    \n",
+    "    # adjust saved name for file to include time interval bounds\n",
+    "    an.save['paired']['prefix'] = base_prefix+'_'+t[0].strftime('%Y%m%d')+'_'+t[1].strftime('%Y%m%d')\n",
+    "    an.save_analysis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6ba95ad-f73a-4b68-8ae5-7858eee6c970",
+   "metadata": {},
+   "source": [
+    "Notes regarding satellite pairing methods:\n",
+    "- some additional development needed to deal with time chuncking. Some OMPS NM orbit files cross the day. This doesn't cause issues if it is within the specified time_interval, but data past the time_interval in the file will be dropped and currently will not be read in during the next time_interval\n",
+    "\n",
+    "- Satellite pairing bilinearly interpolates model data in time to satellite observation times. When observations are before (after) the first (last) model file, time interpolation is nearest-neighbor and only the first (last) file is used. Right now the time-interpolation does not take into account if processing is being done over time chunks. Impact should be minimal, as observations have been filtered to be within time_intervals and the model file subsetter should be selecting all the necessary files."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ddb4e95-1db0-4f01-b433-2d279b1d25fe",
+   "metadata": {},
+   "source": [
+    "### Read in saved paired data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce25d669-166a-42a0-861e-ceef59b26d34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "an = driver.analysis()\n",
+    "an.control = 'control_omps_nm-raqms.yaml'\n",
+    "an.read_control()\n",
+    "an.read_analysis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f41b0217-c3cf-4cde-996e-d6149cea5aa5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "#an.paired['omps_nm_raqms'].obj = an.paired['omps_nm_raqms'].obj.swap_dims({'time':'x'})\n",
+    "an.paired['omps_nm_raqms'].obj['o3vmr'].values[np.isnan(an.paired['omps_nm_raqms'].obj['ozone_column'].values)] = np.nan\n",
+    "an.paired['omps_nm_raqms'].obj['o3vmr'].values[an.paired['omps_nm_raqms'].obj['o3vmr'].values < 50] = np.nan\n",
+    "an.paired['omps_nm_raqms'].obj['ozone_column'].values[np.isnan(an.paired['omps_nm_raqms'].obj['o3vmr'].values)] = np.nan\n",
+    "an.plotting()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dev_monet",
+   "language": "python",
+   "name": "develop_monet"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/jupyter_notebooks/Monet-example-raqms-aeronet.ipynb b/examples/jupyter_notebooks/Monet-example-raqms-aeronet.ipynb
new file mode 100644
index 00000000..4d65c66e
--- /dev/null
+++ b/examples/jupyter_notebooks/Monet-example-raqms-aeronet.ipynb
@@ -0,0 +1,20436 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fb6cc3d7",
+   "metadata": {},
+   "source": [
+    "# MELODIES-MONET dev\n",
+    "\n",
+    "This example illustrates MELODIES-MONET capabilities through analyzing the performance of FV3-RAQMS model runs relative to AERONET observations.\n",
+    "\n",
+    "First, import the driver class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "72ac7242",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-15T17:10:58.595853Z",
+     "start_time": "2021-11-15T17:10:56.478030Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mbruckner/miniconda3/envs/develop_monet/lib/python3.9/site-packages/pyproj/__init__.py:89: UserWarning: pyproj unable to set database path.\n",
+      "  _pyproj_global_context_initialize()\n"
+     ]
+    }
+   ],
+   "source": [
+    "from melodies_monet import driver"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ad609bd",
+   "metadata": {},
+   "source": [
+    "### Driver Class\n",
+    "\n",
+    "Now lets create an instance of the python driver analysis class. It consists of 4 main parts; model instances, observation instances, a paired instance of both. This will allow us to move things around the plotting function for spatial and overlays and more complex plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "45a8c17f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-15T17:10:58.600619Z",
+     "start_time": "2021-11-15T17:10:58.597927Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "an = driver.analysis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a61b852b",
+   "metadata": {},
+   "source": [
+    "### Control File\n",
+    "set the yaml control fire and begin by reading the file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "76a765c9",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-15T17:10:58.629784Z",
+     "start_time": "2021-11-15T17:10:58.602047Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "an.control = 'control_raqms.yaml'\n",
+    "an.read_control()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "131430bb",
+   "metadata": {},
+   "source": [
+    "### Loading the model data\n",
+    "\n",
+    "driver will automatically loop through the \"models\" found in the model section of the yaml file and create an instance of the driver.model class for each that includes the label, mapping information, and xarray object as well as the filenames. Note it can open multiple files easily by including hot keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "db656cb1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-15T17:12:21.985802Z",
+     "start_time": "2021-11-15T17:10:58.631843Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "control\n",
+      "{'files': '/ships19/aqda/mbruckner/monet_plots/linked_control/tracer*nc', 'mod_type': 'fv3raqms', 'radius_of_influence': 19500, 'mapping': {'aeronet': {'aod': 'aod_550nm'}}, 'projection': 'None', 'plot_kwargs': {'color': 'dodgerblue', 'marker': '^', 'linestyle': '-'}}\n",
+      "fv3raqms\n",
+      "/ships19/aqda/mbruckner/monet_plots/linked_control/tracer*nc\n",
+      "gocart_aod\n",
+      "{'files': '/ships19/models2/lenzen/FV3GFS.9.0.2019/O3.VIIRS.GOCART_AODFRACTION/C192/5DEGLL/tracer*nc', 'mod_type': 'fv3raqms', 'radius_of_influence': 19500, 'mapping': {'aeronet': {'aod': 'aod_550nm'}}, 'projection': 'None', 'plot_kwargs': {'color': 'goldenrod', 'marker': '^', 'linestyle': '-'}}\n",
+      "fv3raqms\n",
+      "/ships19/models2/lenzen/FV3GFS.9.0.2019/O3.VIIRS.GOCART_AODFRACTION/C192/5DEGLL/tracer*nc\n"
+     ]
+    }
+   ],
+   "source": [
+    "an.open_models()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9d952059",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-15T17:12:22.772084Z",
+     "start_time": "2021-11-15T17:12:21.987512Z"
+    }
+   },
+   "outputs": [
+    {
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:     (time: 241, tile: 6, x: 720, y: 361, z: 64)\n",
+       "Coordinates:\n",
+       "  * time        (time) datetime64[ns] 2019-07-01T18:00:00 ... 2019-08-30T18:0...\n",
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+       "    latitude    (y, x) float64 -90.0 -90.0 -90.0 -90.0 ... 90.0 90.0 90.0 90.0\n",
+       "Dimensions without coordinates: tile\n",
+       "Data variables: (12/158)\n",
+       "    imin        (time, tile) int32 dask.array&lt;chunksize=(1, 6), meta=np.ndarray&gt;\n",
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+       "    ...          ...\n",
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+       "    covermx     (time, z, y, x) float32 dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;\n",
+       "    oxvermx     (time, z, y, x) float32 dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    CDATE:        2019070118\n",
+       "    from:         fv32ll.gen.gen.deflate.f90\n",
+       "    case:         C192\n",
+       "    ak:           [0.0000000e+00 0.0000000e+00 5.7500000e-01 5.7410000e+00 2....\n",
+       "    bk:           [1.0000000e+00 9.9467119e-01 9.8862660e-01 9.8174229e-01 9....\n",
+       "    forecast_hr:  6.0\n",
+       "    timestep:     450.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b5effedd-8de9-4816-bf6d-6e42a05d0ae7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b5effedd-8de9-4816-bf6d-6e42a05d0ae7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 241</li><li><span>tile</span>: 6</li><li><span class='xr-has-index'>x</span>: 720</li><li><span class='xr-has-index'>y</span>: 361</li><li><span class='xr-has-index'>z</span>: 64</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-416e46bf-af30-447c-9c84-73b6a617a8c0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-416e46bf-af30-447c-9c84-73b6a617a8c0' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-07-01T18:00:00 ... 2019-08-...</div><input id='attrs-6e54cb01-8b37-47a3-a837-455999b6fcdb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6e54cb01-8b37-47a3-a837-455999b6fcdb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be5baacc-fe33-4574-a099-11528f0a9ad1' class='xr-var-data-in' type='checkbox'><label for='data-be5baacc-fe33-4574-a099-11528f0a9ad1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-07-01T18:00:00.000000000&#x27;, &#x27;2019-07-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2019-07-02T06:00:00.000000000&#x27;, ..., &#x27;2019-08-30T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2019-08-30T12:00:00.000000000&#x27;, &#x27;2019-08-30T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.5 1.0 1.5 ... -1.5 -1.0 -0.5</div><input id='attrs-79c16e47-f9b9-4f6f-b9db-c3b50cb5dd8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79c16e47-f9b9-4f6f-b9db-c3b50cb5dd8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7716bf5f-ea44-42a9-b530-2826d5fe0b16' class='xr-var-data-in' type='checkbox'><label for='data-7716bf5f-ea44-42a9-b530-2826d5fe0b16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cartesian_axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>degrees_E</dd></dl></div><div class='xr-var-data'><pre>array([ 0. ,  0.5,  1. , ..., -1.5, -1. , -0.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.5 -89.0 ... 89.5 90.0</div><input id='attrs-9b341991-fcfc-475d-a4c8-a86c458aafa6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b341991-fcfc-475d-a4c8-a86c458aafa6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e48fd10-0a44-47f2-9721-c9090e30eb91' class='xr-var-data-in' type='checkbox'><label for='data-4e48fd10-0a44-47f2-9721-c9090e30eb91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cartesian_axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_N</dd></dl></div><div class='xr-var-data'><pre>array([-90. , -89.5, -89. , ...,  89. ,  89.5,  90. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 4.0 ... 62.0 63.0 64.0</div><input id='attrs-244599c7-66f7-4c66-802b-b3b1e76c9229' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-244599c7-66f7-4c66-802b-b3b1e76c9229' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04e48980-37e5-4dfd-9b9b-faddb5e034ad' class='xr-var-data-in' type='checkbox'><label for='data-04e48980-37e5-4dfd-9b9b-faddb5e034ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13., 14.,\n",
+       "       15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28.,\n",
+       "       29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42.,\n",
+       "       43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56.,\n",
+       "       57., 58., 59., 60., 61., 62., 63., 64.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.5 1.0 1.5 ... -1.5 -1.0 -0.5</div><input id='attrs-e1a48626-80bc-4740-b8fb-01dd828bfbe4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e1a48626-80bc-4740-b8fb-01dd828bfbe4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d662ffb-24e7-4d26-ba7f-06717f9699cf' class='xr-var-data-in' type='checkbox'><label for='data-8d662ffb-24e7-4d26-ba7f-06717f9699cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0. ,  0.5,  1. , ..., -1.5, -1. , -0.5],\n",
+       "       [ 0. ,  0.5,  1. , ..., -1.5, -1. , -0.5],\n",
+       "       [ 0. ,  0.5,  1. , ..., -1.5, -1. , -0.5],\n",
+       "       ...,\n",
+       "       [ 0. ,  0.5,  1. , ..., -1.5, -1. , -0.5],\n",
+       "       [ 0. ,  0.5,  1. , ..., -1.5, -1. , -0.5],\n",
+       "       [ 0. ,  0.5,  1. , ..., -1.5, -1. , -0.5]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -90.0 -90.0 ... 90.0 90.0</div><input id='attrs-ca52cdad-780c-45cb-a7c2-b5e94dca7717' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca52cdad-780c-45cb-a7c2-b5e94dca7717' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10c1f328-f29b-41d2-8693-85cb4525bf7a' class='xr-var-data-in' type='checkbox'><label for='data-10c1f328-f29b-41d2-8693-85cb4525bf7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-90. , -90. , -90. , ..., -90. , -90. , -90. ],\n",
+       "       [-89.5, -89.5, -89.5, ..., -89.5, -89.5, -89.5],\n",
+       "       [-89. , -89. , -89. , ..., -89. , -89. , -89. ],\n",
+       "       ...,\n",
+       "       [ 89. ,  89. ,  89. , ...,  89. ,  89. ,  89. ],\n",
+       "       [ 89.5,  89.5,  89.5, ...,  89.5,  89.5,  89.5],\n",
+       "       [ 90. ,  90. ,  90. , ...,  90. ,  90. ,  90. ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5e4a8829-990b-4791-b6c1-0df03f0b336d' class='xr-section-summary-in' type='checkbox'  ><label for='section-5e4a8829-990b-4791-b6c1-0df03f0b336d' class='xr-section-summary' >Data variables: <span>(158)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>imin</span></div><div class='xr-var-dims'>(time, tile)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 6), meta=np.ndarray&gt;</div><input id='attrs-2193a815-3258-46ea-903f-1b26d4a10465' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2193a815-3258-46ea-903f-1b26d4a10465' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5053d064-c097-49a1-b648-c9148ef86916' class='xr-var-data-in' type='checkbox'><label for='data-5053d064-c097-49a1-b648-c9148ef86916' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.65 kiB </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 6) </td>\n",
+       "                        <td> (1, 6) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 964 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> int32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"80\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"30\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"5\" x2=\"30\" y2=\"5\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"30\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"18\" x2=\"30\" y2=\"18\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"30\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"31\" x2=\"30\" y2=\"31\" />\n",
+       "  <line x1=\"0\" y1=\"37\" x2=\"30\" y2=\"37\" />\n",
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+       "  <line x1=\"0\" y1=\"50\" x2=\"30\" y2=\"50\" />\n",
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+       "  <line x1=\"0\" y1=\"62\" x2=\"30\" y2=\"62\" />\n",
+       "  <line x1=\"0\" y1=\"69\" x2=\"30\" y2=\"69\" />\n",
+       "  <line x1=\"0\" y1=\"75\" x2=\"30\" y2=\"75\" />\n",
+       "  <line x1=\"0\" y1=\"81\" x2=\"30\" y2=\"81\" />\n",
+       "  <line x1=\"0\" y1=\"88\" x2=\"30\" y2=\"88\" />\n",
+       "  <line x1=\"0\" y1=\"94\" x2=\"30\" y2=\"94\" />\n",
+       "  <line x1=\"0\" y1=\"100\" x2=\"30\" y2=\"100\" />\n",
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+       "  <line x1=\"0\" y1=\"120\" x2=\"30\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 30.287105057904046,0.0 30.287105057904046,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"15.143553\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6</text>\n",
+       "  <text x=\"50.287105\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,50.287105,60.000000)\">241</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>imax</span></div><div class='xr-var-dims'>(time, tile)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 6), meta=np.ndarray&gt;</div><input id='attrs-2a982b06-b693-4664-a82c-d546f9bd9d08' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a982b06-b693-4664-a82c-d546f9bd9d08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b626052-5181-440f-8d04-ffc31d53e864' class='xr-var-data-in' type='checkbox'><label for='data-3b626052-5181-440f-8d04-ffc31d53e864' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.65 kiB </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 6) </td>\n",
+       "                        <td> (1, 6) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 964 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> int32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"80\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"30\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"5\" x2=\"30\" y2=\"5\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"30\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"18\" x2=\"30\" y2=\"18\" />\n",
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+       "  <line x1=\"0\" y1=\"37\" x2=\"30\" y2=\"37\" />\n",
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+       "  <line x1=\"0\" y1=\"56\" x2=\"30\" y2=\"56\" />\n",
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+       "  <line x1=\"0\" y1=\"120\" x2=\"30\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"15.143553\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6</text>\n",
+       "  <text x=\"50.287105\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,50.287105,60.000000)\">241</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>jmin</span></div><div class='xr-var-dims'>(time, tile)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 6), meta=np.ndarray&gt;</div><input id='attrs-f4dfa910-885b-4534-a0b3-3b5e6bf55154' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4dfa910-885b-4534-a0b3-3b5e6bf55154' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3bb98d7-99cc-427e-b618-2f1318705b4b' class='xr-var-data-in' type='checkbox'><label for='data-f3bb98d7-99cc-427e-b618-2f1318705b4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.65 kiB </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 6) </td>\n",
+       "                        <td> (1, 6) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 964 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> int32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>jmax</span></div><div class='xr-var-dims'>(time, tile)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 6), meta=np.ndarray&gt;</div><input id='attrs-96eeb369-dddc-45c5-bec3-cdb2adeb45c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-96eeb369-dddc-45c5-bec3-cdb2adeb45c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7e645b9-dc10-4aee-b206-69df11f35448' class='xr-var-data-in' type='checkbox'><label for='data-d7e645b9-dc10-4aee-b206-69df11f35448' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.65 kiB </td>\n",
+       "                        <td> 24 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 6) </td>\n",
+       "                        <td> (1, 6) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 964 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> int32 </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aod</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-23a63bc4-7148-4c81-af47-e920dfd8b0c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23a63bc4-7148-4c81-af47-e920dfd8b0c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a507832-1ec9-42c5-b311-7b0d7cfe1965' class='xr-var-data-in' type='checkbox'><label for='data-4a507832-1ec9-42c5-b311-7b0d7cfe1965' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
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+       "                        <td> 723 Tasks </td>\n",
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+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
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+       "            <table>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>incaodgsi</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-15151097-3028-4a15-ab18-4cc7c7862313' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15151097-3028-4a15-ab18-4cc7c7862313' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca22dbc4-a74f-4a2a-9045-28bab867c8da' class='xr-var-data-in' type='checkbox'><label for='data-ca22dbc4-a74f-4a2a-9045-28bab867c8da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gaodbcoc</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-4825c3c6-418d-4bb4-84f5-963ccd6d3474' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4825c3c6-418d-4bb4-84f5-963ccd6d3474' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0da2926d-a49f-4e3f-85ef-6731823b86a5' class='xr-var-data-in' type='checkbox'><label for='data-0da2926d-a49f-4e3f-85ef-6731823b86a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "                </tbody>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gaoddust</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-5365ca57-ada2-46f5-aa8e-2a5a0033cdf9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5365ca57-ada2-46f5-aa8e-2a5a0033cdf9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0397c30-3c6d-451e-a35c-558a50dbb058' class='xr-var-data-in' type='checkbox'><label for='data-f0397c30-3c6d-451e-a35c-558a50dbb058' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gaodssalt</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-4273c49a-c7bc-453c-a788-ed11d91e7399' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4273c49a-c7bc-453c-a788-ed11d91e7399' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3e07986-2bc9-49c9-84b1-49db38ed29dc' class='xr-var-data-in' type='checkbox'><label for='data-d3e07986-2bc9-49c9-84b1-49db38ed29dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gaodsulf</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-537bb40f-f10f-4ba4-b71a-fd81afa43e46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-537bb40f-f10f-4ba4-b71a-fd81afa43e46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db43ff1f-ee7a-46f1-9efb-c91e26c01713' class='xr-var-data-in' type='checkbox'><label for='data-db43ff1f-ee7a-46f1-9efb-c91e26c01713' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aod_bcoc</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-3b85d159-9076-45d5-bedd-558d96a02b03' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b85d159-9076-45d5-bedd-558d96a02b03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c26f7580-e344-4323-b5db-4e1935a49173' class='xr-var-data-in' type='checkbox'><label for='data-c26f7580-e344-4323-b5db-4e1935a49173' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aodssalt</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-48956f20-93d1-4c62-9623-86efc31d6607' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48956f20-93d1-4c62-9623-86efc31d6607' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2079e219-75ae-46bd-aba8-645b79ad5824' class='xr-var-data-in' type='checkbox'><label for='data-2079e219-75ae-46bd-aba8-645b79ad5824' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "            </table>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aoddust</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-cee121a0-e19f-4cee-aa4f-144f22c6e495' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cee121a0-e19f-4cee-aa4f-144f22c6e495' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05a22eda-aabc-49b3-9316-6402411f4416' class='xr-var-data-in' type='checkbox'><label for='data-05a22eda-aabc-49b3-9316-6402411f4416' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aodsulf</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-84d8d7f7-c111-41a6-bd2a-55f2da56b8e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84d8d7f7-c111-41a6-bd2a-55f2da56b8e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7fad7de-70e9-4f54-ac58-60771ef00b56' class='xr-var-data-in' type='checkbox'><label for='data-a7fad7de-70e9-4f54-ac58-60771ef00b56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dqdtsum</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-22f9fed9-2cd2-4bf6-8fbe-d1c72fd12c23' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22f9fed9-2cd2-4bf6-8fbe-d1c72fd12c23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cf69817-310f-4fe5-8afa-ab9033c1c760' class='xr-var-data-in' type='checkbox'><label for='data-0cf69817-310f-4fe5-8afa-ab9033c1c760' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cotot</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-86661a78-8090-423f-9b65-d0253353e119' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86661a78-8090-423f-9b65-d0253353e119' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-608524dd-023f-4d3e-b6d6-c631fd0b41e2' class='xr-var-data-in' type='checkbox'><label for='data-608524dd-023f-4d3e-b6d6-c631fd0b41e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>10**18 molec</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>no2tot</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-5445624a-8cc7-440a-9ee9-a4e1d61ff848' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5445624a-8cc7-440a-9ee9-a4e1d61ff848' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1cdbfe3-1f89-4248-98ba-4b50049b00b9' class='xr-var-data-in' type='checkbox'><label for='data-a1cdbfe3-1f89-4248-98ba-4b50049b00b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>10**18 molec</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sulftotinc</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-56292b1b-0e8c-45b5-94da-5fa4e69fe4a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56292b1b-0e8c-45b5-94da-5fa4e69fe4a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c131daf-22cf-4123-a0be-f5e1c1b18c98' class='xr-var-data-in' type='checkbox'><label for='data-2c131daf-22cf-4123-a0be-f5e1c1b18c98' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>10**18 molec</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>persulfinc</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-85325d8a-6752-4f30-810e-5acd074e599c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85325d8a-6752-4f30-810e-5acd074e599c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa89aba5-813e-4dd6-8142-6e5a3c46b932' class='xr-var-data-in' type='checkbox'><label for='data-aa89aba5-813e-4dd6-8142-6e5a3c46b932' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>percent</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sfcp</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-cc5e5b37-e0a9-4257-a324-5e0dd4ddbc89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc5e5b37-e0a9-4257-a324-5e0dd4ddbc89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b1d7433-2311-4726-90b0-1fb2735955f4' class='xr-var-data-in' type='checkbox'><label for='data-0b1d7433-2311-4726-90b0-1fb2735955f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mb</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sfcz</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-3b65e54e-fe48-443d-8e05-a9d488e441ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b65e54e-fe48-443d-8e05-a9d488e441ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63d156a0-27f4-486f-a52c-1dc4d2c7d525' class='xr-var-data-in' type='checkbox'><label for='data-63d156a0-27f4-486f-a52c-1dc4d2c7d525' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>o3dep</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-73c2089c-1a88-43c6-a024-d4a094aa9f13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73c2089c-1a88-43c6-a024-d4a094aa9f13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-797619ea-8285-44a5-b064-1677d710d437' class='xr-var-data-in' type='checkbox'><label for='data-797619ea-8285-44a5-b064-1677d710d437' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv day -1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zlwigrd</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-d553e566-2018-4cd7-b3bb-97386ea8b835' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d553e566-2018-4cd7-b3bb-97386ea8b835' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41598643-e8ff-46ee-8b57-4170ff2400e0' class='xr-var-data-in' type='checkbox'><label for='data-41598643-e8ff-46ee-8b57-4170ff2400e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cumprec</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-a6270162-6372-40b1-88b8-13be8a190dbf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6270162-6372-40b1-88b8-13be8a190dbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50558799-68a4-4da0-8fd7-40396546d8ae' class='xr-var-data-in' type='checkbox'><label for='data-50558799-68a4-4da0-8fd7-40396546d8ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cumprel</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-096032c7-14cd-4ea3-a651-a274a73bf0c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-096032c7-14cd-4ea3-a651-a274a73bf0c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab67afe4-e1af-410f-a823-263d6ce836c5' class='xr-var-data-in' type='checkbox'><label for='data-ab67afe4-e1af-410f-a823-263d6ce836c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prec</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-48954372-22e9-42b1-b57b-d526fdca8454' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48954372-22e9-42b1-b57b-d526fdca8454' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-553a1d12-c38a-430e-b94b-826e28550985' class='xr-var-data-in' type='checkbox'><label for='data-553a1d12-c38a-430e-b94b-826e28550985' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm day-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prel</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-25e7f0d8-747f-4e2e-9935-ab9e1dfae373' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25e7f0d8-747f-4e2e-9935-ab9e1dfae373' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6947a97-4034-40cf-bce3-370252aff0e5' class='xr-var-data-in' type='checkbox'><label for='data-f6947a97-4034-40cf-bce3-370252aff0e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mm day-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>codep</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-bed2d22b-f664-4228-8cf4-5b7af00cf6fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bed2d22b-f664-4228-8cf4-5b7af00cf6fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-213d4731-495e-4c94-ae38-cca622f2f53e' class='xr-var-data-in' type='checkbox'><label for='data-213d4731-495e-4c94-ae38-cca622f2f53e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv day -1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>colno2</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-406b2c53-85b3-429f-867e-d94fc53af66e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-406b2c53-85b3-429f-867e-d94fc53af66e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38c7a418-abe3-4c72-92be-bacbe1700a97' class='xr-var-data-in' type='checkbox'><label for='data-38c7a418-abe3-4c72-92be-bacbe1700a97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>colnoy</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-d7de9eaa-412a-422c-8bed-3ad06dedd297' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7de9eaa-412a-422c-8bed-3ad06dedd297' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a79421b-d640-4b35-8cca-a9a617957f40' class='xr-var-data-in' type='checkbox'><label for='data-7a79421b-d640-4b35-8cca-a9a617957f40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tau2d</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-b42f9df7-a550-4658-829a-f442571c2762' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b42f9df7-a550-4658-829a-f442571c2762' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77001a9a-e273-40c8-8b29-e9bf2d711f70' class='xr-var-data-in' type='checkbox'><label for='data-77001a9a-e273-40c8-8b29-e9bf2d711f70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>srcno</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-6f839cee-9b97-45ba-9cc0-42148779cf03' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f839cee-9b97-45ba-9cc0-42148779cf03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed2f214c-9bd6-4954-8908-6c2616b47687' class='xr-var-data-in' type='checkbox'><label for='data-ed2f214c-9bd6-4954-8908-6c2616b47687' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lnox2d</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-83ff6a49-f065-4e92-accf-7c9b7d3c63d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83ff6a49-f065-4e92-accf-7c9b7d3c63d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc927633-15bc-4e88-a8d7-3ed13a34b035' class='xr-var-data-in' type='checkbox'><label for='data-fc927633-15bc-4e88-a8d7-3ed13a34b035' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>noydep</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-a418f870-15b7-4cc1-8bff-6d05c4691531' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a418f870-15b7-4cc1-8bff-6d05c4691531' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d37e6bc1-ed68-451a-af6a-f8dab0cbf3d8' class='xr-var-data-in' type='checkbox'><label for='data-d37e6bc1-ed68-451a-af6a-f8dab0cbf3d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv day -1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slmsk</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-bc8f8269-f953-4fa1-b631-3912cf43ee34' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc8f8269-f953-4fa1-b631-3912cf43ee34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ad906db-f8ea-470a-b2eb-f2375baf7fe9' class='xr-var-data-in' type='checkbox'><label for='data-2ad906db-f8ea-470a-b2eb-f2375baf7fe9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>table :</span></dt><dd>0-ocean-lake, 1-land, 2-seaice</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
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+       "                    <tr>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vtype</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-c52ad64b-a6a9-4fb3-8f41-88a59ed876b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c52ad64b-a6a9-4fb3-8f41-88a59ed876b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52f18820-05a7-440a-9b59-42086ffb4c1b' class='xr-var-data-in' type='checkbox'><label for='data-52f18820-05a7-440a-9b59-42086ffb4c1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>source :</span></dt><dd>Modified_IGBP_MODIS_NOAH</dd><dt><span>table1 :</span></dt><dd>0-ocean,lake,1-everneedlelforest,2-everbroadlforest,3-decneedlefforest,4-decbroadlforest</dd><dt><span>table2 :</span></dt><dd>5-mixedforest,6-closedshrub,7-openshrub,8-woodysavana,9-savannas,10-grasslands</dd><dt><span>table3 :</span></dt><dd>11-permanent wetlands,12-cropland,13-surban,14-cropland/natural veg</dd><dt><span>table4 :</span></dt><dd>15-ice,16-barren, 17-water,18-wooded tundra,19-mixed tundra,20-barren tundra</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stype</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-7f8fcfe4-e58f-4ee1-8041-19692e0b48c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f8fcfe4-e58f-4ee1-8041-19692e0b48c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f625031f-5a30-48e4-98c7-781eef56e516' class='xr-var-data-in' type='checkbox'><label for='data-f625031f-5a30-48e4-98c7-781eef56e516' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>table1 :</span></dt><dd>0-ocean,1-sand,2-loamy sand,3-sandy loam</dd><dt><span>table2 :</span></dt><dd>4-silt loam,5-silt,6-loam,7-sandy clay loam</dd><dt><span>table3 :</span></dt><dd>8-silty clay loam,9-clay loam,10-sandy clay</dd><dt><span>table4 :</span></dt><dd>11-silty clay,12-clay,13-organic material,14-water</dd><dt><span>table5 :</span></dt><dd>15-bedrock,16-other(land-ice)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 238.96 MiB </td>\n",
+       "                        <td> 0.99 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 361, 720) </td>\n",
+       "                        <td> (1, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-a085040f-fe48-4c17-b0b1-a1bfca3c4149' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a085040f-fe48-4c17-b0b1-a1bfca3c4149' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5d537ad-97a7-42f1-824a-4651644671bc' class='xr-var-data-in' type='checkbox'><label for='data-b5d537ad-97a7-42f1-824a-4651644671bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>o3mrphys</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-7d538a46-ac48-422f-b298-74f0d90da9eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d538a46-ac48-422f-b298-74f0d90da9eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fae2012-648c-411c-8a19-a0f287e121a3' class='xr-var-data-in' type='checkbox'><label for='data-7fae2012-648c-411c-8a19-a0f287e121a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>o3vmr</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-e102ad6d-9831-4d71-a435-321a343ff2e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e102ad6d-9831-4d71-a435-321a343ff2e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6446c43a-584e-4de5-9c38-1dcbebdf2c47' class='xr-var-data-in' type='checkbox'><label for='data-6446c43a-584e-4de5-9c38-1dcbebdf2c47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>potvort</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-362fa192-95e6-4245-9cea-a6a2d27ed88d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-362fa192-95e6-4245-9cea-a6a2d27ed88d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e094511-8fbb-4677-b768-2e9727acf0f0' class='xr-var-data-in' type='checkbox'><label for='data-5e094511-8fbb-4677-b768-2e9727acf0f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vort</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-3de81362-2196-476f-878d-13a7a063c8f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3de81362-2196-476f-878d-13a7a063c8f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-736ab2a0-2a38-46fa-9986-3b716ce280aa' class='xr-var-data-in' type='checkbox'><label for='data-736ab2a0-2a38-46fa-9986-3b716ce280aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>absvort</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-6bad9bce-5d5d-49c1-9bd4-259356710ecf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bad9bce-5d5d-49c1-9bd4-259356710ecf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a70c5a04-25bf-48cd-93e1-4e8fa8a171f3' class='xr-var-data-in' type='checkbox'><label for='data-a70c5a04-25bf-48cd-93e1-4e8fa8a171f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zdash</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-88f65be6-bf65-4250-8994-7aa3b492e637' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88f65be6-bf65-4250-8994-7aa3b492e637' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6fea9f4-89b9-4b33-bd80-79dd981bd464' class='xr-var-data-in' type='checkbox'><label for='data-b6fea9f4-89b9-4b33-bd80-79dd981bd464' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dpm</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-9d0b9012-ef23-4957-8dea-c8857932a726' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d0b9012-ef23-4957-8dea-c8857932a726' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92455eb9-c57a-4974-91ff-5e20f1fe0b56' class='xr-var-data-in' type='checkbox'><label for='data-92455eb9-c57a-4974-91ff-5e20f1fe0b56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>mb</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dz</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-a4c5a7ed-8297-4cf9-9d79-1e98097027e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4c5a7ed-8297-4cf9-9d79-1e98097027e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bf24c40-706c-4375-a979-34f7eba20d9b' class='xr-var-data-in' type='checkbox'><label for='data-3bf24c40-706c-4375-a979-34f7eba20d9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-911d3dc0-7778-4470-91bb-f621c284105a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-911d3dc0-7778-4470-91bb-f621c284105a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a625aa9a-ec4f-4c2f-ad25-9b2cf955376a' class='xr-var-data-in' type='checkbox'><label for='data-a625aa9a-ec4f-4c2f-ad25-9b2cf955376a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-550f1158-b0c4-4c5b-bbd1-01b03febef38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-550f1158-b0c4-4c5b-bbd1-01b03febef38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20e6819d-f78d-44dd-b1ec-d414d50012d7' class='xr-var-data-in' type='checkbox'><label for='data-20e6819d-f78d-44dd-b1ec-d414d50012d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m/s</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>V</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-a1ee51bb-cc28-41ea-8caa-6903c2d5081d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1ee51bb-cc28-41ea-8caa-6903c2d5081d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc6f1e23-0ca4-41b1-95ce-806814f6cb59' class='xr-var-data-in' type='checkbox'><label for='data-fc6f1e23-0ca4-41b1-95ce-806814f6cb59' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m/s</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ext_sulf</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-881b7d3f-9cf3-4a23-baf2-509fef6a5315' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-881b7d3f-9cf3-4a23-baf2-509fef6a5315' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dc59ea0-2f89-4652-96d1-266a532ae23f' class='xr-var-data-in' type='checkbox'><label for='data-0dc59ea0-2f89-4652-96d1-266a532ae23f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ext_bcoc</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-164dc554-5139-4217-82cc-eab70bebdc27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-164dc554-5139-4217-82cc-eab70bebdc27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-944e27ac-052d-49fa-9341-f573d44341a6' class='xr-var-data-in' type='checkbox'><label for='data-944e27ac-052d-49fa-9341-f573d44341a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ext_dust</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-30149be4-fb65-489a-9ddd-78766155d32d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30149be4-fb65-489a-9ddd-78766155d32d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ca94065-b79a-47c9-8b12-594804bc0851' class='xr-var-data-in' type='checkbox'><label for='data-9ca94065-b79a-47c9-8b12-594804bc0851' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ext_seas</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-58dc2ef2-c103-4eea-9514-f489f4fab046' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58dc2ef2-c103-4eea-9514-f489f4fab046' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7ffa6dc-8e50-47a5-9387-64a899a0f85b' class='xr-var-data-in' type='checkbox'><label for='data-a7ffa6dc-8e50-47a5-9387-64a899a0f85b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ext_tot</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-52d7bcba-02ac-4bec-9ed9-f540e72d60c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52d7bcba-02ac-4bec-9ed9-f540e72d60c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93f9db33-4373-4c69-b9da-219e5501164e' class='xr-var-data-in' type='checkbox'><label for='data-93f9db33-4373-4c69-b9da-219e5501164e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cvdqdt</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-06ecea6d-f435-4ea4-8051-04c8d03128d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06ecea6d-f435-4ea4-8051-04c8d03128d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d355133-3ef8-423a-9d16-5f66ce68b167' class='xr-var-data-in' type='checkbox'><label for='data-0d355133-3ef8-423a-9d16-5f66ce68b167' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>incsulf</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-7f975661-89cf-441b-b6c1-b7453a7a4957' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f975661-89cf-441b-b6c1-b7453a7a4957' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbb0719c-d895-434e-b070-17096641afca' class='xr-var-data-in' type='checkbox'><label for='data-dbb0719c-d895-434e-b070-17096641afca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>units</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>incbc1</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-e7b08061-2ae0-445c-b5e9-c7bd94f5485d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7b08061-2ae0-445c-b5e9-c7bd94f5485d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac22b56f-1d6f-416f-9163-33af980508e3' class='xr-var-data-in' type='checkbox'><label for='data-ac22b56f-1d6f-416f-9163-33af980508e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>units</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>incbc2</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-542e3cbd-8fb8-4243-b2a2-317db255e67a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-542e3cbd-8fb8-4243-b2a2-317db255e67a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d36bf324-f42f-4862-b2ed-6939d3c42ea2' class='xr-var-data-in' type='checkbox'><label for='data-d36bf324-f42f-4862-b2ed-6939d3c42ea2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>units</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
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+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
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+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>incoc1</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-09575b46-a463-45c1-abe4-99830c70252f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09575b46-a463-45c1-abe4-99830c70252f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87d3ac97-5233-4414-a142-5a04afb15fd0' class='xr-var-data-in' type='checkbox'><label for='data-87d3ac97-5233-4414-a142-5a04afb15fd0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>units</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>incoc2</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-a4e51160-d54a-4d44-90ae-9040a96c3fa4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4e51160-d54a-4d44-90ae-9040a96c3fa4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af8e3fff-32a0-42be-9e6a-f5f3951a6ac8' class='xr-var-data-in' type='checkbox'><label for='data-af8e3fff-32a0-42be-9e6a-f5f3951a6ac8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>units</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>incdust1</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-292c6fbf-3d53-48f3-8dfd-fee13b8a3acc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-292c6fbf-3d53-48f3-8dfd-fee13b8a3acc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3445f5d-f086-4817-93fa-669da5bbcb6a' class='xr-var-data-in' type='checkbox'><label for='data-c3445f5d-f086-4817-93fa-669da5bbcb6a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>units</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>incdust2</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-46a17778-a2e3-469d-808c-6150c9c83002' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46a17778-a2e3-469d-808c-6150c9c83002' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fea4c605-0e66-4311-8988-bad2d99dcde4' class='xr-var-data-in' type='checkbox'><label for='data-fea4c605-0e66-4311-8988-bad2d99dcde4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>units</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>incdust3</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-4db8db83-af71-44a2-b4a0-bc7b46f9fb72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4db8db83-af71-44a2-b4a0-bc7b46f9fb72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c2a1dfb-662f-4a62-a598-ed8fac78e36f' class='xr-var-data-in' type='checkbox'><label for='data-6c2a1dfb-662f-4a62-a598-ed8fac78e36f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>units</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>incdust4</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-71145915-dfa0-44e6-aa13-5ef861380393' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71145915-dfa0-44e6-aa13-5ef861380393' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24a62074-b256-4083-aa4c-e5e2620c545a' class='xr-var-data-in' type='checkbox'><label for='data-24a62074-b256-4083-aa4c-e5e2620c545a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>units</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>incdust5</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-db9ab35c-6c96-48ae-9372-e0d41d6f606d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db9ab35c-6c96-48ae-9372-e0d41d6f606d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-def48654-8167-4d64-8179-820e62afb8af' class='xr-var-data-in' type='checkbox'><label for='data-def48654-8167-4d64-8179-820e62afb8af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>units</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>incseas1</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-5965d96a-79b8-4d99-88b2-f90d26a58655' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5965d96a-79b8-4d99-88b2-f90d26a58655' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4522e235-204f-406d-ad14-525f5e6d6345' class='xr-var-data-in' type='checkbox'><label for='data-4522e235-204f-406d-ad14-525f5e6d6345' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>units</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>incseas2</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-37f2982f-7bd1-4d78-861f-bdddde5dcc1c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37f2982f-7bd1-4d78-861f-bdddde5dcc1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-418ee8aa-6887-4cb4-a4ad-38b7bf8a870a' class='xr-var-data-in' type='checkbox'><label for='data-418ee8aa-6887-4cb4-a4ad-38b7bf8a870a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>units</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>incseas3</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-bde3e808-6abe-49c6-80d5-caaabac7d146' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bde3e808-6abe-49c6-80d5-caaabac7d146' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64d8650a-351a-4241-9791-d3bd899c556b' class='xr-var-data-in' type='checkbox'><label for='data-64d8650a-351a-4241-9791-d3bd899c556b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>units</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>incseas4</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-6215354a-eead-4655-81ce-b0d252727459' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6215354a-eead-4655-81ce-b0d252727459' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-051c3673-a15f-493b-b225-6df3c93e6b91' class='xr-var-data-in' type='checkbox'><label for='data-051c3673-a15f-493b-b225-6df3c93e6b91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>units</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>brcl</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-81454b01-69e2-43ea-96b0-5839e3943bac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81454b01-69e2-43ea-96b0-5839e3943bac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed319cb0-e1dc-45d7-aaa0-6307a27da8a8' class='xr-var-data-in' type='checkbox'><label for='data-ed319cb0-e1dc-45d7-aaa0-6307a27da8a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>brno3</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-aca994e0-680b-4c42-baee-fe665a5c43d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aca994e0-680b-4c42-baee-fe665a5c43d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea9275f2-4aba-4782-8b85-204155421941' class='xr-var-data-in' type='checkbox'><label for='data-ea9275f2-4aba-4782-8b85-204155421941' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bry</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-28fd716c-20be-466b-a0c2-a887b9193c3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28fd716c-20be-466b-a0c2-a887b9193c3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-865af463-7bd5-4ec5-9490-e66f571fff7b' class='xr-var-data-in' type='checkbox'><label for='data-865af463-7bd5-4ec5-9490-e66f571fff7b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ccl4</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-12f58bd9-1f43-43b1-ad6c-66678a64cc46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12f58bd9-1f43-43b1-ad6c-66678a64cc46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5406c038-3bf1-4590-8b92-91ed20a6ebc2' class='xr-var-data-in' type='checkbox'><label for='data-5406c038-3bf1-4590-8b92-91ed20a6ebc2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmrp</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-d489054e-86b6-4850-88f7-88d3522757c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d489054e-86b6-4850-88f7-88d3522757c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e92e186-2b30-4ef1-a974-afcec9b85c0e' class='xr-var-data-in' type='checkbox'><label for='data-8e92e186-2b30-4ef1-a974-afcec9b85c0e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prdp</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-e8cbebe9-38a0-4741-b960-f0dbebc2b7fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8cbebe9-38a0-4741-b960-f0dbebc2b7fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e78ec64f-7b4b-4b17-995f-9f2875a6758d' class='xr-var-data-in' type='checkbox'><label for='data-e78ec64f-7b4b-4b17-995f-9f2875a6758d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ch2o</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-fec68f3d-7a81-499d-865a-a43b13eb1bda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fec68f3d-7a81-499d-865a-a43b13eb1bda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e30053e-2a61-460e-8979-fda1f2f04175' class='xr-var-data-in' type='checkbox'><label for='data-9e30053e-2a61-460e-8979-fda1f2f04175' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ch3br</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-c359ad32-cdd4-494f-a717-ad5eb3794d3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c359ad32-cdd4-494f-a717-ad5eb3794d3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-090b5664-537f-4afc-a354-049c67e12784' class='xr-var-data-in' type='checkbox'><label for='data-090b5664-537f-4afc-a354-049c67e12784' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ch3cl</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-16979b2a-b619-4e1b-87d6-9024b02ab928' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16979b2a-b619-4e1b-87d6-9024b02ab928' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ed9d6f8-4fb0-475d-9364-d73db0105442' class='xr-var-data-in' type='checkbox'><label for='data-6ed9d6f8-4fb0-475d-9364-d73db0105442' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ch3ooh</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-c6721cdf-25c2-4587-9f98-4f52b9d0d3ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6721cdf-25c2-4587-9f98-4f52b9d0d3ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7985fb37-a30a-4e51-b604-79b7d0bfdd8d' class='xr-var-data-in' type='checkbox'><label for='data-7985fb37-a30a-4e51-b604-79b7d0bfdd8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ch4</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-3c6b42a1-aefa-4da4-865d-058ab1f15a0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c6b42a1-aefa-4da4-865d-058ab1f15a0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73cf5d03-b729-41de-bc1e-6b40c314af3f' class='xr-var-data-in' type='checkbox'><label for='data-73cf5d03-b729-41de-bc1e-6b40c314af3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cl2</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-759f3004-3441-4ce0-a3a1-c4ed0a7fe6ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-759f3004-3441-4ce0-a3a1-c4ed0a7fe6ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-320f1d9f-bac4-46d2-af5a-14010b35a271' class='xr-var-data-in' type='checkbox'><label for='data-320f1d9f-bac4-46d2-af5a-14010b35a271' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clno3</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-41f82fc1-0e37-4c96-ac7f-1a699752a535' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41f82fc1-0e37-4c96-ac7f-1a699752a535' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea5832ff-bfe2-4265-af22-dd2167e13e9d' class='xr-var-data-in' type='checkbox'><label for='data-ea5832ff-bfe2-4265-af22-dd2167e13e9d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cly</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-181ba1de-3db1-4002-8586-3a62c83c591a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-181ba1de-3db1-4002-8586-3a62c83c591a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-335adcc3-3e65-4e5b-a1c7-1fd31b4a20c6' class='xr-var-data-in' type='checkbox'><label for='data-335adcc3-3e65-4e5b-a1c7-1fd31b4a20c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>co</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-e6a8b415-a3b9-47a7-b0d4-5e3ccf1fc842' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6a8b415-a3b9-47a7-b0d4-5e3ccf1fc842' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9863361a-a522-468a-96b0-d5c266fd2c61' class='xr-var-data-in' type='checkbox'><label for='data-9863361a-a522-468a-96b0-d5c266fd2c61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>f1211</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-0c997491-80e5-4263-ae52-5e5a04619478' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c997491-80e5-4263-ae52-5e5a04619478' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e535878-209d-4860-a44e-546a5f2058e9' class='xr-var-data-in' type='checkbox'><label for='data-7e535878-209d-4860-a44e-546a5f2058e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>f12</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-f29e488d-71a0-4b84-bc61-4cc31dda16e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f29e488d-71a0-4b84-bc61-4cc31dda16e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66d2dae4-785a-4c1e-8ec2-1472da0dcbc3' class='xr-var-data-in' type='checkbox'><label for='data-66d2dae4-785a-4c1e-8ec2-1472da0dcbc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>f1301</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-109fd659-a25b-4641-a138-d366b084c4e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-109fd659-a25b-4641-a138-d366b084c4e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8ec3cde-a194-479d-aa78-209153501eba' class='xr-var-data-in' type='checkbox'><label for='data-d8ec3cde-a194-479d-aa78-209153501eba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h2o2</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-29d3b381-7db3-4884-9afd-97bb1163753a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29d3b381-7db3-4884-9afd-97bb1163753a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7dd0924b-1db7-4aac-8091-fff0d948a287' class='xr-var-data-in' type='checkbox'><label for='data-7dd0924b-1db7-4aac-8091-fff0d948a287' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vrp</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-4a27f014-7432-4114-9c55-d0f753432a1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a27f014-7432-4114-9c55-d0f753432a1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18649378-8cdd-4cfa-b199-6e16a8938b6c' class='xr-var-data-in' type='checkbox'><label for='data-18649378-8cdd-4cfa-b199-6e16a8938b6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hbr</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-f199dc1c-77f1-4034-b787-d500ea26cd0f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f199dc1c-77f1-4034-b787-d500ea26cd0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-043d5215-aabc-4438-9d70-b15ba35dfbba' class='xr-var-data-in' type='checkbox'><label for='data-043d5215-aabc-4438-9d70-b15ba35dfbba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hcl</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-2f48a39b-1c6f-4836-ac62-c4cdf9e87e5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f48a39b-1c6f-4836-ac62-c4cdf9e87e5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c52291c2-6a72-451d-b4f5-c35b22ec0b2d' class='xr-var-data-in' type='checkbox'><label for='data-c52291c2-6a72-451d-b4f5-c35b22ec0b2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rip</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-897d4eba-4cb9-44a1-bcf4-20b68650833a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-897d4eba-4cb9-44a1-bcf4-20b68650833a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-488debbb-f02b-4b05-a081-0e9a66439430' class='xr-var-data-in' type='checkbox'><label for='data-488debbb-f02b-4b05-a081-0e9a66439430' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hno3t</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-68d269a1-55ce-4067-9e46-1d6bfa0e594a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68d269a1-55ce-4067-9e46-1d6bfa0e594a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5eddad5-c6c2-4dbb-8a7e-9b3fb74b7d17' class='xr-var-data-in' type='checkbox'><label for='data-b5eddad5-c6c2-4dbb-8a7e-9b3fb74b7d17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hno4</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-ef95c602-c921-4398-a062-f8f37dd72e32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef95c602-c921-4398-a062-f8f37dd72e32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95ead6c4-7a3f-4964-aab3-1beebc33087d' class='xr-var-data-in' type='checkbox'><label for='data-95ead6c4-7a3f-4964-aab3-1beebc33087d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hobr</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-21106577-f4bb-41db-adc5-c5e68ecf2213' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21106577-f4bb-41db-adc5-c5e68ecf2213' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d882f9a-3442-4af5-8852-e179ca3d28ae' class='xr-var-data-in' type='checkbox'><label for='data-7d882f9a-3442-4af5-8852-e179ca3d28ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hocl</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-835d3ace-2047-42ea-ae7a-43ecf045b205' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-835d3ace-2047-42ea-ae7a-43ecf045b205' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1b9ce0c-7602-4b3f-bb68-607b84e62f67' class='xr-var-data-in' type='checkbox'><label for='data-e1b9ce0c-7602-4b3f-bb68-607b84e62f67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mtcfm</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-c57c40ed-028b-4c46-ad47-550711581428' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c57c40ed-028b-4c46-ad47-550711581428' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5508be3e-915b-428b-8f37-c94bbe4738a7' class='xr-var-data-in' type='checkbox'><label for='data-5508be3e-915b-428b-8f37-c94bbe4738a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n2o5</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-8beaf746-5bf3-493c-8cc4-7a5b3bf91bc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8beaf746-5bf3-493c-8cc4-7a5b3bf91bc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67110c16-0968-4576-a6ff-f721c97779b0' class='xr-var-data-in' type='checkbox'><label for='data-67110c16-0968-4576-a6ff-f721c97779b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n2o</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-72a324f3-e077-4eda-b21e-088e09a4b8a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72a324f3-e077-4eda-b21e-088e09a4b8a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a616509-3f7b-4776-8b43-88ecf14a077b' class='xr-var-data-in' type='checkbox'><label for='data-7a616509-3f7b-4776-8b43-88ecf14a077b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>no2</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-98220d9e-a61e-4afe-96b1-9f8ccd95b2a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98220d9e-a61e-4afe-96b1-9f8ccd95b2a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4cd816c-f20c-4ef5-940b-57b9bb98a95e' class='xr-var-data-in' type='checkbox'><label for='data-b4cd816c-f20c-4ef5-940b-57b9bb98a95e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>no3</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-33c2d10a-5033-414d-8e68-8073480f1ee5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33c2d10a-5033-414d-8e68-8073480f1ee5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-524c896e-94fb-49c8-b75f-5114b4424cef' class='xr-var-data-in' type='checkbox'><label for='data-524c896e-94fb-49c8-b75f-5114b4424cef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>noy</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-5c86bbab-1b58-4994-9600-6d2521d6beca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c86bbab-1b58-4994-9600-6d2521d6beca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c63b2e3-1626-482f-8130-26f13971beb0' class='xr-var-data-in' type='checkbox'><label for='data-0c63b2e3-1626-482f-8130-26f13971beb0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>oclo</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-ded81697-ddea-4d0d-846f-273ffb547ba2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ded81697-ddea-4d0d-846f-273ffb547ba2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35be3738-b569-41cc-9c03-a18430918c9d' class='xr-var-data-in' type='checkbox'><label for='data-35be3738-b569-41cc-9c03-a18430918c9d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ox</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-a26d0646-5ad8-4bd9-b1d2-7460f348ffdc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a26d0646-5ad8-4bd9-b1d2-7460f348ffdc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89a7e767-3cd8-46d1-b3a2-b48b3805fc22' class='xr-var-data-in' type='checkbox'><label for='data-89a7e767-3cd8-46d1-b3a2-b48b3805fc22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>c2h6</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-c48dbfef-cf9e-4e01-ac26-453cf5b2a5cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c48dbfef-cf9e-4e01-ac26-453cf5b2a5cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a62a8df-f3bb-4fc8-a2fd-4b0a4e1be96a' class='xr-var-data-in' type='checkbox'><label for='data-4a62a8df-f3bb-4fc8-a2fd-4b0a4e1be96a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ald2</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-b3322ce9-a450-4435-91b1-d6c20c1a5e2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3322ce9-a450-4435-91b1-d6c20c1a5e2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96e60f3e-9506-40b5-b904-f0bf517eb963' class='xr-var-data-in' type='checkbox'><label for='data-96e60f3e-9506-40b5-b904-f0bf517eb963' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ethooh</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-845807c3-3706-466b-be3d-45baa16c6236' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-845807c3-3706-466b-be3d-45baa16c6236' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-991f361c-e019-4758-9e92-edaafe7b51be' class='xr-var-data-in' type='checkbox'><label for='data-991f361c-e019-4758-9e92-edaafe7b51be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pan</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-2bdd7611-eaf6-4c05-a846-4b2daf720a88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2bdd7611-eaf6-4c05-a846-4b2daf720a88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e57a804d-57c6-475d-8ddc-32f7259cdfd1' class='xr-var-data-in' type='checkbox'><label for='data-e57a804d-57c6-475d-8ddc-32f7259cdfd1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>par</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-615acc1c-441a-46db-9089-23ea84359149' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-615acc1c-441a-46db-9089-23ea84359149' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e01b8da-fedd-466d-bddf-fd50412c80d3' class='xr-var-data-in' type='checkbox'><label for='data-7e01b8da-fedd-466d-bddf-fd50412c80d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>onit</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-5ae55270-533a-46b6-b8a3-1add5a14bc07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ae55270-533a-46b6-b8a3-1add5a14bc07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23aaf840-9236-464c-8145-ede6cbebe4c0' class='xr-var-data-in' type='checkbox'><label for='data-23aaf840-9236-464c-8145-ede6cbebe4c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aone</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-ddda3e37-6602-4b5e-a899-8807ea8052a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ddda3e37-6602-4b5e-a899-8807ea8052a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-102b7bae-50d8-496a-97cc-2863fa7be034' class='xr-var-data-in' type='checkbox'><label for='data-102b7bae-50d8-496a-97cc-2863fa7be034' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rooh</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-23ee5fb0-d1d6-4f74-8434-20a88df7106d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23ee5fb0-d1d6-4f74-8434-20a88df7106d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f644848-5f38-4319-9d1b-25ebaa2cf42c' class='xr-var-data-in' type='checkbox'><label for='data-9f644848-5f38-4319-9d1b-25ebaa2cf42c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mgly</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-dc02dfdc-94fb-4c8c-b832-0d1ecc433c9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc02dfdc-94fb-4c8c-b832-0d1ecc433c9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cdcc2c0-5025-47b9-9301-aa6b1d204a78' class='xr-var-data-in' type='checkbox'><label for='data-4cdcc2c0-5025-47b9-9301-aa6b1d204a78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eth</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-0fa9699f-5950-445b-9ccf-c897e06bd0e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0fa9699f-5950-445b-9ccf-c897e06bd0e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80279de2-ed3a-4b32-a167-98e9ab459112' class='xr-var-data-in' type='checkbox'><label for='data-80279de2-ed3a-4b32-a167-98e9ab459112' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>olet</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-059ea2ce-7e85-41db-b3d9-c92d94e15c84' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-059ea2ce-7e85-41db-b3d9-c92d94e15c84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fb09587-4871-494f-ba72-25a9eb728a3d' class='xr-var-data-in' type='checkbox'><label for='data-5fb09587-4871-494f-ba72-25a9eb728a3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>isop</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-31f264d0-490e-4e89-a0d5-79c041a7da5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31f264d0-490e-4e89-a0d5-79c041a7da5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d88f2cea-cea6-4c49-8f9f-0fe9093f5598' class='xr-var-data-in' type='checkbox'><label for='data-d88f2cea-cea6-4c49-8f9f-0fe9093f5598' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>isoprd</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-772c9b7e-8b6b-4e7e-92ce-eabe36aa6af2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-772c9b7e-8b6b-4e7e-92ce-eabe36aa6af2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c281d5ad-b366-4c0e-b5be-338f85026b8b' class='xr-var-data-in' type='checkbox'><label for='data-c281d5ad-b366-4c0e-b5be-338f85026b8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>propar</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-b6ae7a52-d2c9-44c3-bc57-481f7e8c71df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6ae7a52-d2c9-44c3-bc57-481f7e8c71df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2359c223-0e5a-4f65-8ec8-2ec7aa521750' class='xr-var-data-in' type='checkbox'><label for='data-2359c223-0e5a-4f65-8ec8-2ec7aa521750' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ch3oh</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-50700907-e924-4e8e-9559-198aaa8ce3e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50700907-e924-4e8e-9559-198aaa8ce3e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb772ef2-3f80-412d-90f8-405514e7e0c4' class='xr-var-data-in' type='checkbox'><label for='data-cb772ef2-3f80-412d-90f8-405514e7e0c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mvk</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-4d84003e-224d-458a-aa91-e5920897b472' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d84003e-224d-458a-aa91-e5920897b472' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-234b16c0-b238-48a9-8f1e-fda5835ad090' class='xr-var-data-in' type='checkbox'><label for='data-234b16c0-b238-48a9-8f1e-fda5835ad090' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
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+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>macr</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-bc37699d-b91a-429e-977f-f8752a932ca8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc37699d-b91a-429e-977f-f8752a932ca8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90c4ff86-988d-4d0b-9a81-f5f292e84de2' class='xr-var-data-in' type='checkbox'><label for='data-90c4ff86-988d-4d0b-9a81-f5f292e84de2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mpan</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-4bbc36a3-d400-4b7a-813a-50ecbc7eac1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bbc36a3-d400-4b7a-813a-50ecbc7eac1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa0f1726-f73c-4bfe-a8b9-6de9e2d91727' class='xr-var-data-in' type='checkbox'><label for='data-aa0f1726-f73c-4bfe-a8b9-6de9e2d91727' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hcn</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-f50039dd-c12d-4543-96e2-e408d0cca1d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f50039dd-c12d-4543-96e2-e408d0cca1d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85fd9a13-114b-4013-b8bd-e974e3f73471' class='xr-var-data-in' type='checkbox'><label for='data-85fd9a13-114b-4013-b8bd-e974e3f73471' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ch3cn</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-ab045d2a-07e3-4d12-a945-bfb98169b089' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab045d2a-07e3-4d12-a945-bfb98169b089' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e31ac41e-4b5a-464c-a660-7fd7fffde3e6' class='xr-var-data-in' type='checkbox'><label for='data-e31ac41e-4b5a-464c-a660-7fd7fffde3e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>so2</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-c6b8f586-3728-4b6e-8cf3-2ccdaf41b9fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6b8f586-3728-4b6e-8cf3-2ccdaf41b9fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82a9b966-7081-4e6d-be9a-19d6be389160' class='xr-var-data-in' type='checkbox'><label for='data-82a9b966-7081-4e6d-be9a-19d6be389160' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sulf</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-374f7945-ea74-4b76-867e-e232b56c4511' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-374f7945-ea74-4b76-867e-e232b56c4511' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5241e12-ee1e-4381-930e-ae9a75aaf622' class='xr-var-data-in' type='checkbox'><label for='data-e5241e12-ee1e-4381-930e-ae9a75aaf622' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ug/kg-drya</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DMS</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-999d5c4c-e481-4333-80f1-7cb28c4d8a35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-999d5c4c-e481-4333-80f1-7cb28c4d8a35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-945c8f05-def1-4599-be58-f1d253bfca54' class='xr-var-data-in' type='checkbox'><label for='data-945c8f05-def1-4599-be58-f1d253bfca54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>msa</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-dd181f69-4537-4b58-90c3-e7358c48cac4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd181f69-4537-4b58-90c3-e7358c48cac4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa542b26-332e-4c4c-b713-17f9c0ea4fea' class='xr-var-data-in' type='checkbox'><label for='data-fa542b26-332e-4c4c-b713-17f9c0ea4fea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bc1</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-435bc4f4-3d8c-4c49-93ea-4f11513a9e0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-435bc4f4-3d8c-4c49-93ea-4f11513a9e0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4045e1b4-b7cb-49dc-ba93-e306a250dabd' class='xr-var-data-in' type='checkbox'><label for='data-4045e1b4-b7cb-49dc-ba93-e306a250dabd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ug/kg-drya</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bc2</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-ddd69262-bfb5-43d3-8d8d-d6bc714981c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ddd69262-bfb5-43d3-8d8d-d6bc714981c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cc65a13-f5ca-44ef-aed7-b1d17ff47ae4' class='xr-var-data-in' type='checkbox'><label for='data-2cc65a13-f5ca-44ef-aed7-b1d17ff47ae4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ug/kg-drya</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>oc1</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-4b1d9366-f08e-4cb7-9f1f-3f5506980c13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b1d9366-f08e-4cb7-9f1f-3f5506980c13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df6c02d9-6d88-4c91-a48b-4a2362878718' class='xr-var-data-in' type='checkbox'><label for='data-df6c02d9-6d88-4c91-a48b-4a2362878718' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ug/kg-drya</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>oc2</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-337e5dd5-4112-47ad-9944-4c393dbbfbe3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-337e5dd5-4112-47ad-9944-4c393dbbfbe3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50af2d5d-56cd-451b-91a4-f9915355d217' class='xr-var-data-in' type='checkbox'><label for='data-50af2d5d-56cd-451b-91a4-f9915355d217' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ug/kg-drya</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dust1</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-1ed63dca-2f01-4b3e-b123-e3fe51334ec0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ed63dca-2f01-4b3e-b123-e3fe51334ec0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2fc3130-bd0e-42b6-8874-55b7f93d183c' class='xr-var-data-in' type='checkbox'><label for='data-d2fc3130-bd0e-42b6-8874-55b7f93d183c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ug/kg-drya</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dust2</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-c4491256-84fc-460b-9065-6460dda694db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4491256-84fc-460b-9065-6460dda694db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-728ee23e-a5e5-424f-9e22-d5b121a93e4b' class='xr-var-data-in' type='checkbox'><label for='data-728ee23e-a5e5-424f-9e22-d5b121a93e4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ug/kg-drya</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dust3</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-1653cde0-00a1-4a99-99a7-c12bc73f2b9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1653cde0-00a1-4a99-99a7-c12bc73f2b9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-279b239f-8f18-4729-a85b-1a688f916904' class='xr-var-data-in' type='checkbox'><label for='data-279b239f-8f18-4729-a85b-1a688f916904' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ug/kg-drya</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dust4</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-017fcf86-a558-426b-86bc-fccec35fb0e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-017fcf86-a558-426b-86bc-fccec35fb0e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15ff9e29-f819-4919-b3bc-00a265b6325d' class='xr-var-data-in' type='checkbox'><label for='data-15ff9e29-f819-4919-b3bc-00a265b6325d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ug/kg-drya</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dust5</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-39da64c2-99fc-4971-8949-fea187a7cc4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39da64c2-99fc-4971-8949-fea187a7cc4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f3d1234-c598-4414-81de-0be1d6dfc1f9' class='xr-var-data-in' type='checkbox'><label for='data-2f3d1234-c598-4414-81de-0be1d6dfc1f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ug/kg-drya</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seas1</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-9efb4caf-2f5e-4fdd-93c3-5a4d1e27fc57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9efb4caf-2f5e-4fdd-93c3-5a4d1e27fc57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b33e9852-60ab-4ba8-ab88-f05a7e01ca93' class='xr-var-data-in' type='checkbox'><label for='data-b33e9852-60ab-4ba8-ab88-f05a7e01ca93' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ug/kg-drya</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seas2</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-7630a09e-306b-4d0d-bf10-b2cafbf512a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7630a09e-306b-4d0d-bf10-b2cafbf512a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55e59a3d-573b-4a7b-950a-31ed1bd31d0f' class='xr-var-data-in' type='checkbox'><label for='data-55e59a3d-573b-4a7b-950a-31ed1bd31d0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ug/kg-drya</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seas3</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-d6fc066b-1f73-43d0-b6ef-3fc48617585d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6fc066b-1f73-43d0-b6ef-3fc48617585d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24fb0820-eb39-406c-a811-b1dbd05338b2' class='xr-var-data-in' type='checkbox'><label for='data-24fb0820-eb39-406c-a811-b1dbd05338b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ug/kg-drya</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seas4</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-271e1348-aecd-44ae-91a6-2e013316a1a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-271e1348-aecd-44ae-91a6-2e013316a1a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91991c80-0667-4a56-be0e-b8e6fb751a6e' class='xr-var-data-in' type='checkbox'><label for='data-91991c80-0667-4a56-be0e-b8e6fb751a6e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ug/kg-drya</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>seas5</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-e9471d77-7d4d-48bb-81f5-5d3f7d23e72f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9471d77-7d4d-48bb-81f5-5d3f7d23e72f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71a4d3ed-0009-4dbf-8c98-982636f149cb' class='xr-var-data-in' type='checkbox'><label for='data-71a4d3ed-0009-4dbf-8c98-982636f149cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ug/kg-drya</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>coanth25</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-c0efcc83-b639-4fdf-ae5f-769043d558c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0efcc83-b639-4fdf-ae5f-769043d558c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5dc221f-f820-4b5d-9da0-90ebafc1520c' class='xr-var-data-in' type='checkbox'><label for='data-d5dc221f-f820-4b5d-9da0-90ebafc1520c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bbcod25</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-4b4fbaaa-5aad-4580-b7ad-fdf9ac2169d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b4fbaaa-5aad-4580-b7ad-fdf9ac2169d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b96dfcde-1651-4088-98ff-ed4146dda14a' class='xr-var-data-in' type='checkbox'><label for='data-b96dfcde-1651-4088-98ff-ed4146dda14a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>br2</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-0bfaef45-dade-4af0-ab2f-ee8171bd6757' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0bfaef45-dade-4af0-ab2f-ee8171bd6757' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0fe4383-6108-4f0a-8546-d904ea14e387' class='xr-var-data-in' type='checkbox'><label for='data-d0fe4383-6108-4f0a-8546-d904ea14e387' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppv</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>theta</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-5385b0cc-f633-4b2b-9bec-d98a6836657a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5385b0cc-f633-4b2b-9bec-d98a6836657a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6abb8c1-02ec-4ffe-8a0e-7305af7b91f9' class='xr-var-data-in' type='checkbox'><label for='data-e6abb8c1-02ec-4ffe-8a0e-7305af7b91f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>no2ten</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-2e920e65-9414-4d5f-9bba-5e4c66d8cead' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e920e65-9414-4d5f-9bba-5e4c66d8cead' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a91580ae-3c47-431b-b895-c7a3afe12479' class='xr-var-data-in' type='checkbox'><label for='data-a91580ae-3c47-431b-b895-c7a3afe12479' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lnox</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-de260c26-edac-4928-82bc-dd1c8f91c244' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de260c26-edac-4928-82bc-dd1c8f91c244' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b44da27-f248-4eab-bed0-ee6143dda28e' class='xr-var-data-in' type='checkbox'><label for='data-4b44da27-f248-4eab-bed0-ee6143dda28e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>jo1d</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-83d32fe4-914b-454f-bba5-0dbc41a82830' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83d32fe4-914b-454f-bba5-0dbc41a82830' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d72a9d3-82f2-4deb-9f14-3810e8991cb3' class='xr-var-data-in' type='checkbox'><label for='data-7d72a9d3-82f2-4deb-9f14-3810e8991cb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>jno</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-9715aecd-24df-48c8-9b87-5c9cbacdc93b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9715aecd-24df-48c8-9b87-5c9cbacdc93b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f2d29ac-4ebd-4238-904f-bd385e057585' class='xr-var-data-in' type='checkbox'><label for='data-7f2d29ac-4ebd-4238-904f-bd385e057585' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cot6hr</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-82d54c04-bade-434e-965d-abebdaab1ce3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82d54c04-bade-434e-965d-abebdaab1ce3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e770bed3-bcd6-407a-a0d1-e653abe41725' class='xr-var-data-in' type='checkbox'><label for='data-e770bed3-bcd6-407a-a0d1-e653abe41725' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>emcofire</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-c6440de9-0017-4e40-982a-1fd7606e935e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6440de9-0017-4e40-982a-1fd7606e935e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acd7e878-0dec-465a-9b7c-10eb00a6b32b' class='xr-var-data-in' type='checkbox'><label for='data-acd7e878-0dec-465a-9b7c-10eb00a6b32b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>covermx</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-5b9054e1-38b2-412e-9b0b-9537d137b2c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b9054e1-38b2-412e-9b0b-9537d137b2c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70daf23e-99da-435a-ae21-473e5a22ef4b' class='xr-var-data-in' type='checkbox'><label for='data-70daf23e-99da-435a-ae21-473e5a22ef4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>oxvermx</span></div><div class='xr-var-dims'>(time, z, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 64, 361, 720), meta=np.ndarray&gt;</div><input id='attrs-9b1afe60-ee74-4f64-8d71-fafe41e58f06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b1afe60-ee74-4f64-8d71-fafe41e58f06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02c42fc9-2361-4eae-b096-98d24f3fb4ca' class='xr-var-data-in' type='checkbox'><label for='data-02c42fc9-2361-4eae-b096-98d24f3fb4ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 14.93 GiB </td>\n",
+       "                        <td> 63.46 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (241, 64, 361, 720) </td>\n",
+       "                        <td> (1, 64, 361, 720) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 723 Tasks </td>\n",
+       "                        <td> 241 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> float32 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
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+       " 5.5712000e+01 1.1689900e+02 2.1401500e+02 3.5622300e+02 5.5272000e+02\n",
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+       " 7.9569080e+03 9.0570510e+03 1.0171712e+04 1.1276348e+04 1.2344490e+04\n",
+       " 1.3348671e+04 1.4261435e+04 1.5056342e+04 1.5708893e+04 1.6197315e+04\n",
+       " 1.6503144e+04 1.6611603e+04 1.6511736e+04 1.6197967e+04 1.5683489e+04\n",
+       " 1.4993074e+04 1.4154316e+04 1.3197065e+04 1.2152937e+04 1.1054853e+04\n",
+       " 9.9366140e+03 8.8325370e+03 7.7771500e+03 6.8048740e+03 5.9370500e+03\n",
+       " 5.1671460e+03 4.4854930e+03 3.8830520e+03 3.3514600e+03 2.8830380e+03\n",
+       " 2.4707880e+03 2.1083660e+03 1.7900510e+03 1.5107110e+03 1.2657520e+03\n",
+       " 1.0510800e+03 8.6305800e+02 6.9845700e+02 5.5442400e+02 4.2843400e+02\n",
+       " 3.1826600e+02 2.2195800e+02 1.3779000e+02 6.4247000e+01 2.0000000e+01]</dd><dt><span>bk :</span></dt><dd>[1.0000000e+00 9.9467119e-01 9.8862660e-01 9.8174229e-01 9.7386760e-01\n",
+       " 9.6482757e-01 9.5443411e-01 9.4249106e-01 9.2879731e-01 9.1315100e-01\n",
+       " 8.9535499e-01 8.7522360e-01 8.5259067e-01 8.2731884e-01 7.9930974e-01\n",
+       " 7.6851468e-01 7.3494523e-01 6.9868292e-01 6.5988704e-01 6.1879962e-01\n",
+       " 5.7574665e-01 5.3113482e-01 4.8544331e-01 4.3921080e-01 3.9301826e-01\n",
+       " 3.4746849e-01 3.0316412e-01 2.6068545e-01 2.2057019e-01 1.8329624e-01\n",
+       " 1.4926877e-01 1.1881219e-01 9.2166910e-02 6.9474580e-02 5.0646840e-02\n",
+       " 3.5441620e-02 2.3555880e-02 1.4637120e-02 8.2940200e-03 4.1067100e-03\n",
+       " 1.6359100e-03 4.3106000e-04 3.6970000e-05 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]</dd><dt><span>forecast_hr :</span></dt><dd>6.0</dd><dt><span>timestep :</span></dt><dd>450.0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:     (time: 241, tile: 6, x: 720, y: 361, z: 64)\n",
+       "Coordinates:\n",
+       "  * time        (time) datetime64[ns] 2019-07-01T18:00:00 ... 2019-08-30T18:0...\n",
+       "  * x           (x) float64 0.0 0.5 1.0 1.5 2.0 2.5 ... -2.5 -2.0 -1.5 -1.0 -0.5\n",
+       "  * y           (y) float64 -90.0 -89.5 -89.0 -88.5 ... 88.5 89.0 89.5 90.0\n",
+       "  * z           (z) float64 1.0 2.0 3.0 4.0 5.0 6.0 ... 60.0 61.0 62.0 63.0 64.0\n",
+       "    longitude   (y, x) float64 0.0 0.5 1.0 1.5 2.0 ... -2.5 -2.0 -1.5 -1.0 -0.5\n",
+       "    latitude    (y, x) float64 -90.0 -90.0 -90.0 -90.0 ... 90.0 90.0 90.0 90.0\n",
+       "Dimensions without coordinates: tile\n",
+       "Data variables: (12/158)\n",
+       "    imin        (time, tile) int32 dask.array<chunksize=(1, 6), meta=np.ndarray>\n",
+       "    imax        (time, tile) int32 dask.array<chunksize=(1, 6), meta=np.ndarray>\n",
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+       "    jmax        (time, tile) int32 dask.array<chunksize=(1, 6), meta=np.ndarray>\n",
+       "    aod         (time, y, x) float32 dask.array<chunksize=(1, 361, 720), meta=np.ndarray>\n",
+       "    aodgsi      (time, y, x) float32 dask.array<chunksize=(1, 361, 720), meta=np.ndarray>\n",
+       "    ...          ...\n",
+       "    jo1d        (time, z, y, x) float32 dask.array<chunksize=(1, 64, 361, 720), meta=np.ndarray>\n",
+       "    jno         (time, z, y, x) float32 dask.array<chunksize=(1, 64, 361, 720), meta=np.ndarray>\n",
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+       "    covermx     (time, z, y, x) float32 dask.array<chunksize=(1, 64, 361, 720), meta=np.ndarray>\n",
+       "    oxvermx     (time, z, y, x) float32 dask.array<chunksize=(1, 64, 361, 720), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    CDATE:        2019070118\n",
+       "    from:         fv32ll.gen.gen.deflate.f90\n",
+       "    case:         C192\n",
+       "    ak:           [0.0000000e+00 0.0000000e+00 5.7500000e-01 5.7410000e+00 2....\n",
+       "    bk:           [1.0000000e+00 9.9467119e-01 9.8862660e-01 9.8174229e-01 9....\n",
+       "    forecast_hr:  6.0\n",
+       "    timestep:     450.0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "an.models['gocart_aod'].obj"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c5cb53d",
+   "metadata": {},
+   "source": [
+    "### Open Obs\n",
+    "\n",
+    "Now for melodies-monet we will open preprocessed data in either netcdf icartt or some other format. We will not be retrieving data like monetio does for some observations (ie aeronet, airnow, etc....). Instead we will provide utitilies to do this so that users can add more data easily.\n",
+    "\n",
+    "Like models we list all obs objects in the yaml file and it will loop through and create driver.observation instances that include the model type, file, objects (i.e. data object) and label"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e29efb45",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-15T17:12:22.883619Z",
+     "start_time": "2021-11-15T17:12:22.773762Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "an.open_obs()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eca1c1af",
+   "metadata": {},
+   "source": [
+    "### Pair data, generate plots, calculate stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "dbea6a2c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-15T17:13:16.164091Z",
+     "start_time": "2021-11-15T17:12:22.885337Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[########################################] | 100% Completed |  7.6s\n",
+      "[########################################] | 100% Completed |  7.7s\n",
+      "[########################################] | 100% Completed |  7.8s\n",
+      "[########################################] | 100% Completed |  7.8s\n",
+      "[########################################] | 100% Completed | 22.5s\n",
+      "[########################################] | 100% Completed | 22.6s\n",
+      "[########################################] | 100% Completed | 22.7s\n",
+      "[########################################] | 100% Completed | 22.7s\n"
+     ]
+    }
+   ],
+   "source": [
+    "an.pair_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b96569d5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-15T17:14:37.291818Z",
+     "start_time": "2021-11-15T17:13:16.166054Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: vdiff_plot not specified for aod_550nm, so default used.\n",
+      "Warning: vmin_plot and vmax_plot not specified for aod_550nm, so default used.\n",
+      "[########################################] | 100% Completed |  7.5s\n",
+      "[########################################] | 100% Completed |  7.5s\n",
+      "[########################################] | 100% Completed |  7.6s\n",
+      "[########################################] | 100% Completed |  7.7s\n",
+      "[########################################] | 100% Completed |  0.4s\n",
+      "[########################################] | 100% Completed |  0.5s\n",
+      "[########################################] | 100% Completed |  0.5s\n",
+      "[########################################] | 100% Completed |  0.5s\n",
+      "[########################################] | 100% Completed |  0.5s\n",
+      "[########################################] | 100% Completed |  0.6s\n",
+      "[########################################] | 100% Completed |  0.6s\n",
+      "[########################################] | 100% Completed |  0.7s\n",
+      "[########################################] | 100% Completed |  0.5s\n",
+      "[########################################] | 100% Completed |  0.5s\n",
+      "[########################################] | 100% Completed |  0.5s\n",
+      "[########################################] | 100% Completed |  0.6s\n",
+      "[########################################] | 100% Completed |  0.4s\n",
+      "[########################################] | 100% Completed |  0.5s\n",
+      "[########################################] | 100% Completed |  0.5s\n",
+      "[########################################] | 100% Completed |  0.5s\n",
+      "Warning: vmin_plot and vmax_plot not specified for aod_550nm, so default used.\n",
+      "[########################################] | 100% Completed |  8.0s\n",
+      "[########################################] | 100% Completed |  8.1s\n",
+      "[########################################] | 100% Completed |  8.2s\n",
+      "[########################################] | 100% Completed |  8.2s\n",
+      "[########################################] | 100% Completed |  0.5s\n",
+      "[########################################] | 100% Completed |  0.5s\n",
+      "[########################################] | 100% Completed |  0.6s\n",
+      "[########################################] | 100% Completed |  0.7s\n",
+      "[########################################] | 100% Completed |  1.7s\n",
+      "[########################################] | 100% Completed |  1.7s\n",
+      "[########################################] | 100% Completed |  1.8s\n",
+      "[########################################] | 100% Completed |  1.9s\n",
+      "[########################################] | 100% Completed |  0.5s\n",
+      "[########################################] | 100% Completed |  0.6s\n",
+      "[########################################] | 100% Completed |  0.6s\n",
+      "[########################################] | 100% Completed |  0.7s\n",
+      "[########################################] | 100% Completed |  0.5s\n",
+      "[########################################] | 100% Completed |  0.6s\n",
+      "[########################################] | 100% Completed |  0.6s\n",
+      "[########################################] | 100% Completed |  0.7s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "an.plotting()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ca4ed773",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-15T17:15:08.697870Z",
+     "start_time": "2021-11-15T17:14:37.293628Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "an.stats()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3324ad6",
+   "metadata": {},
+   "source": [
+    "### Additional plots: scatter plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6eaf43a7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-15T17:15:08.706663Z",
+     "start_time": "2021-11-15T17:15:08.702641Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy import stats\n",
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1f8fb0c8",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-15T17:15:09.126450Z",
+     "start_time": "2021-11-15T17:15:08.708425Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "binb = np.linspace(0,4,100)\n",
+    "ct, xedge,ydedge,n=stats.binned_statistic_2d(an.paired['aeronet_gocart_aod'].obj['aod'].values.flatten(),an.paired['aeronet_gocart_aod'].obj['aod_550nm'].values.flatten(),None,'count',bins = binb)\n",
+    "x,y = np.meshgrid(xedge[:-1],ydedge[:-1])\n",
+    "ct[ct ==0] = np.nan\n",
+    "c = plt.scatter(x,y,c=np.log10(ct.T),cmap='viridis')#,alpha=0.2,vmin=0)#,vmax=vmaxs)\n",
+    "\n",
+    "plt.xlabel('High Quality VIIRS AOD Assimilation aod')\n",
+    "plt.ylabel('AERONET aod_550nm')\n",
+    "plt.colorbar(c,label='log(count)')\n",
+    "plt.plot(xedge,ydedge,c='grey',linestyle='dashed')\n",
+    "plt.xlim(-0.1,4)\n",
+    "plt.ylim(-0.1,4)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "31abbe24",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-15T17:15:09.504602Z",
+     "start_time": "2021-11-15T17:15:09.128123Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "binb = np.linspace(0,4,100)\n",
+    "ct, xedge,ydedge,n=stats.binned_statistic_2d(an.paired['aeronet_control'].obj['aod'].values.flatten(),an.paired['aeronet_control'].obj['aod_550nm'].values.flatten(),None,'count',bins = binb)\n",
+    "x,y = np.meshgrid(xedge[:-1],ydedge[:-1])\n",
+    "ct[ct ==0] = np.nan    \n",
+    "c = plt.scatter(x,y,c=np.log10(ct.T),cmap='viridis')#,alpha=0.2,vmin=0)#,vmax=vmaxs)\n",
+    "\n",
+    "plt.xlabel('Control run aod')\n",
+    "plt.ylabel('AERONET aod_550nm')\n",
+    "plt.colorbar(c,label='log(count)')\n",
+    "plt.plot(xedge,ydedge,c='grey',linestyle='dashed')\n",
+    "plt.xlim(-0.1,4)\n",
+    "plt.ylim(-0.1,4)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fcf601fc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:miniconda3-monet_py36]",
+   "language": "python",
+   "name": "conda-env-miniconda3-monet_py36-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.13"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/jupyter_notebooks/raqms-files.txt b/examples/jupyter_notebooks/raqms-files.txt
new file mode 100644
index 00000000..2b1eb7fe
--- /dev/null
+++ b/examples/jupyter_notebooks/raqms-files.txt
@@ -0,0 +1,124 @@
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diff --git a/examples/process_gridded_data/control_time_chunking_with_gridded_data.yaml b/examples/process_gridded_data/control_time_chunking_with_gridded_data.yaml
new file mode 100644
index 00000000..618a23de
--- /dev/null
+++ b/examples/process_gridded_data/control_time_chunking_with_gridded_data.yaml
@@ -0,0 +1,44 @@
+analysis:
+  start_time: '2020-01-01'
+  end_time:   '2020-12-31'
+  time_interval: 'MS'
+  output_dir: $HOME/Plots
+  debug: True
+  regrid: False
+  target_grid: $HOME/Data/Grids/cam_grid.nc
+  time_chunking_with_gridded_data: True
+
+obs:
+
+  MOD08_M3:
+    data_format: gridded_eos
+    datadir: $HOME/Data/MOD08_M3
+    obs_type: gridded_data
+    filename: MOD08_M3.AYYYYDDD.061.*_regrid.nc
+    regrid:
+      base_grid: $HOME/Data/Grids/modis_l3_grid.nc
+      method: bilinear
+    variables:
+      AOD_550_Dark_Target_Deep_Blue_Combined_Mean_Mean:
+        fillvalue: -9999
+        scale: 0.001
+        units: none
+
+model:
+
+  MERRA2:
+    data_format: netcdf
+    mod_type: merra2
+    datadir: $HOME/Data/MERRA2
+    files: MERRA2_*.tavgM_2d_aer_Nx.YYYYMM_MM_TOTEXTTAU_regrid.nc4
+    regrid:
+      base_grid: $HOME/Data/Grids/merra2_grid.nc
+      method: bilinear
+    variables:
+      fillvalue: 1.e+15
+      scale: 1.0
+      units: none
+    mapping:
+      MOD08_M3:
+        TOTEXTTAU: AOD_550_Dark_Target_Deep_Blue_Combined_Mean_Mean
+
diff --git a/examples/process_gridded_data/process_time_chunking_with_gridded_data.py b/examples/process_gridded_data/process_time_chunking_with_gridded_data.py
new file mode 100644
index 00000000..9224a652
--- /dev/null
+++ b/examples/process_gridded_data/process_time_chunking_with_gridded_data.py
@@ -0,0 +1,20 @@
+from melodies_monet import driver
+
+an = driver.analysis()
+an.control = 'control_time_chunking_with_gridded_data.yaml'
+an.read_control()
+an.setup_regridders()
+
+for time_interval in an.time_intervals:
+
+    print(time_interval)
+
+    an.open_obs(time_interval=time_interval)
+    an.open_models(time_interval=time_interval)
+
+    print(an.obs)
+    for obs in an.obs:
+        print(an.obs[obs].obj)
+    print(an.models)
+    for mod in an.models:
+        print(an.models[mod].obj)
diff --git a/examples/submit_jobs/run_modis_l2.py b/examples/submit_jobs/run_modis_l2.py
new file mode 100644
index 00000000..abd5473b
--- /dev/null
+++ b/examples/submit_jobs/run_modis_l2.py
@@ -0,0 +1,13 @@
+import os
+import sys
+sys.path.append('../../')
+import driver
+from util.pair_obs import pair_obs
+
+an = driver.analysis()
+an.control = '../yaml/control_modis_l2.yaml'
+an.read_control()
+an.open_obs()
+
+# to be added to the analysis class as an.pair_obs()
+pair_obs(an)
diff --git a/examples/yaml/control_cmaq-rrfs_surface-all-short_test_jupyter.yaml b/examples/yaml/control_cmaq-rrfs_surface-all-short_test_jupyter.yaml
index 70895758..32df4181 100644
--- a/examples/yaml/control_cmaq-rrfs_surface-all-short_test_jupyter.yaml
+++ b/examples/yaml/control_cmaq-rrfs_surface-all-short_test_jupyter.yaml
@@ -163,7 +163,7 @@ plots:
       #rem_obs_by_nan_pct: {'group_var': 'siteid','pct_cutoff': 25,'times':'hourly'} # Groups by group_var, then removes all instances of groupvar where obs variable is > pct_cutoff % nan values
       rem_obs_nan: True # True: Remove all points where model or obs variable is NaN. False: Remove only points where model variable is NaN.
       ts_select_time: 'time_local' #Time used for avg and plotting: Options: 'time' for UTC or 'time_local'
-      ts_avg_window: 'H' # Options: None for no averaging or list pandas resample rule (e.g., 'H', 'D')
+      ts_avg_window: 'H' # pandas resample rule (e.g., 'H', 'D'). No averaging is done if ts_avg_window is null or not specified.
       set_axis: False #If select True, add vmin_plot and vmax_plot for each variable in obs.
   plot_grp2:
     type: 'taylor' # plot type
diff --git a/examples/yaml/control_cmaq-rrfs_surface-all.yaml b/examples/yaml/control_cmaq-rrfs_surface-all.yaml
index ddaac3c0..b252471e 100644
--- a/examples/yaml/control_cmaq-rrfs_surface-all.yaml
+++ b/examples/yaml/control_cmaq-rrfs_surface-all.yaml
@@ -163,7 +163,7 @@ plots:
       #rem_obs_by_nan_pct: {'group_var': 'siteid','pct_cutoff': 25,'times':'hourly'} # Groups by group_var, then removes all instances of groupvar where obs variable is > pct_cutoff % nan values
       rem_obs_nan: True # True: Remove all points where model or obs variable is NaN. False: Remove only points where model variable is NaN.
       ts_select_time: 'time_local' #Time used for avg and plotting: Options: 'time' for UTC or 'time_local'
-      ts_avg_window: 'H' # Options: None for no averaging or list pandas resample rule (e.g., 'H', 'D')
+      ts_avg_window: 'H' # pandas resample rule (e.g., 'H', 'D'). No averaging is done if ts_avg_window is null or not specified.
       set_axis: False #If select True, add vmin_plot and vmax_plot for each variable in obs.
   plot_grp2:
     type: 'taylor' # plot type
diff --git a/examples/yaml/control_cmaq-rrfs_surface.yaml b/examples/yaml/control_cmaq-rrfs_surface.yaml
index 0306004d..1303be32 100644
--- a/examples/yaml/control_cmaq-rrfs_surface.yaml
+++ b/examples/yaml/control_cmaq-rrfs_surface.yaml
@@ -201,7 +201,7 @@ plots:
       #rem_obs_by_nan_pct: {'group_var': 'siteid','pct_cutoff': 25,'times':'hourly'} # Groups by group_var, then removes all instances of groupvar where obs variable is > pct_cutoff % nan values
       rem_obs_nan: True # True: Remove all points where model or obs variable is NaN. False: Remove only points where model variable is NaN.
       ts_select_time: 'time_local' #Time used for avg and plotting: Options: 'time' for UTC or 'time_local'
-      ts_avg_window: 'H' # Options: None for no averaging or list pandas resample rule (e.g., 'H', 'D')
+      ts_avg_window: 'H' # pandas resample rule (e.g., 'H', 'D'). No averaging is done if ts_avg_window is null or not specified.
       set_axis: False #If select True, add vmin_plot and vmax_plot for each variable in obs.
   plot_grp2:
     type: 'taylor' # plot type
diff --git a/examples/yaml/control_cmaq.yaml b/examples/yaml/control_cmaq.yaml
index 3f0c0ce5..12b145bf 100644
--- a/examples/yaml/control_cmaq.yaml
+++ b/examples/yaml/control_cmaq.yaml
@@ -155,7 +155,7 @@ plots:
       #rem_obs_by_nan_pct: {'group_var': 'siteid','pct_cutoff': 25,'times':'hourly'} # Groups by group_var, then removes all instances of groupvar where obs variable is > pct_cutoff % nan values
       rem_obs_nan: True # True: Remove all points where model or obs variable is NaN. False: Remove only points where model variable is NaN.
       ts_select_time: 'time_local' #Time used for avg and plotting: Options: 'time' for UTC or 'time_local'
-      ts_avg_window: 'H' # Options: None for no averaging or list pandas resample rule (e.g., 'H', 'D')
+      ts_avg_window: 'H' # pandas resample rule (e.g., 'H', 'D'). No averaging is done if ts_avg_window is null or not specified.
       set_axis: False #If select True, add vmin_plot and vmax_plot for each variable in obs.
   plot_grp2:
     type: 'taylor' # plot type
diff --git a/examples/yaml/control_mopitt.yaml b/examples/yaml/control_mopitt.yaml
new file mode 100644
index 00000000..4f6014e3
--- /dev/null
+++ b/examples/yaml/control_mopitt.yaml
@@ -0,0 +1,23 @@
+# General Description:  
+# Any key that is specific for a plot type will begin with ts for timeseries, ty for taylor
+# Opt: Specifying the variable or variable group is optional
+# For now all plots except time series average over the analysis window. 
+# Seting axis values - If set_axis = True in data_proc section of each plot_grp the yaxis for the plot will be set based on the values specified in the obs section for each variable. If set_axis is set to False, then defaults will be used. 'vmin_plot' and 'vmax_plot' are needed for 'timeseries', 'spatial_overlay', and 'boxplot'. 'vdiff_plot' is needed for 'spatial_bias' plots and'ty_scale' is needed for 'taylor' plots. 'nlevels' or the number of levels used in the contour plot can also optionally be provided for spatial_overlay plot. If set_axis = True and the proper limits are not provided in the obs section, a warning will print, and the plot will be created using the default limits.
+#-------------------------------------------------------------------
+analysis:
+  start_time: '2020-08-01'
+  end_time:   '2020-08-03'
+  output_dir: /glade/work/buchholz/melodies-monet/output
+  debug: True
+
+#-------------------------------------------------------------------
+# model:
+      
+#-------------------------------------------------------------------
+obs:    
+  mopitt_l3: # obs label
+    filename: /glade/work/buchholz/data/MOPITT/MOP03JM-2020*
+    obs_type: sat_grid_clm
+    variables: null
+
+
diff --git a/examples/yaml/control_omps_limb.yaml b/examples/yaml/control_omps_limb.yaml
new file mode 100644
index 00000000..b0bec8ce
--- /dev/null
+++ b/examples/yaml/control_omps_limb.yaml
@@ -0,0 +1,109 @@
+# General Description:  
+# Any key that is specific for a plot type will begin with ts for timeseries, ty for taylor
+# Opt: Specifying the variable or variable group is optional
+# For now all plots except time series average over the analysis window. 
+# Seting axis values - If set_axis = True in data_proc section of each plot_grp the yaxis for the plot will be set based on the values specified in the obs section for each variable. If set_axis is set to False, then defaults will be used. 'vmin_plot' and 'vmax_plot' are needed for 'timeseries', 'spatial_overlay', and 'boxplot'. 'vdiff_plot' is needed for 'spatial_bias' plots and'ty_scale' is needed for 'taylor' plots. 'nlevels' or the number of levels used in the contour plot can also optionally be provided for spatial_overlay plot. If set_axis = True and the proper limits are not provided in the obs section, a warning will print, and the plot will be created using the default limits.
+analysis:
+  start_time: '2019-08-19-00:00:00' #UTC
+  end_time: '2019-08-20-00:00:00' #UTC
+  debug: True
+model:
+  fv3raqms: # model label
+    files: /ships19/aqda/lenzen/FV3GFS.8.9.EXP.ivy.PROD.450/O3.BOTH.PSSAS.NGAC.ZBOC1.198/C192/5DEGLL/2019081912/*nc
+    mod_type: 'fv3raqms'
+    
+    radius_of_influence: 12000 #meters
+    #variables: #Opt
+    #  CO:
+    #    unit_scale: 1000.0
+    #    unit_scale_method: '*'
+    mapping: #model species name : obs species name
+      omps_limb: 
+        o3vmr: o3_vis #The mapping tables need to contain the same species for all models.
+    projection: None
+    plot_kwargs: #Opt 
+      color: 'dodgerblue'
+      marker: '+'
+      linestyle: '-.'
+obs:
+  omps_limb: # obs label
+    filename: /ships19/aqda/mbruckner/OMPS-NPP/O3-daily/2019/limb/OMPS-NPP_LP-L2-O3-DAILY_v2.5_2019m0819_2019m0820t151227.h5
+    obs_type: sat_swath_prof
+    
+    variables: #Opt 
+      
+plots:
+  plot_grp1:
+    type: 'timeseries' # plot type
+    fig_kwargs: #Opt to define figure options
+      figsize: [12,6] # figure size if multiple plots
+    default_plot_kwargs: # Opt to define defaults for all plots. Model kwargs overwrite these.
+      linewidth: 2.0
+      markersize: 10.
+    text_kwargs: #Opt
+      fontsize: 18.
+    domain_type: ['all','epa_region'] #List of domain types: 'all' or any domain in obs file. (e.g., airnow: epa_region, state_name, siteid, etc.) 
+    domain_name: ['CONUS','R1'] #List of domain names. If domain_type = all domain_name is used in plot title.
+    data: ['airnow_cmaq_oper','airnow_cmaq_expt','airnow_wrfchem_v4.0'] # make this a list of pairs in obs_model where the obs is the obs label and model is the model_label
+    data_proc:
+      rem_obs_nan: True # True: Remove all points where model or obs variable is NaN. False: Remove only points where model variable is NaN.
+      ts_select_time: 'time_local' #Time used for avg and plotting: Options: 'time' for UTC or 'time_local'
+      ts_avg_window: 'H' # Options: None for no averaging or list pandas resample rule (e.g., 'H', 'D')
+      set_axis: False #If select True, add vmin_plot and vmax_plot for each variable in obs.
+  plot_grp2:
+    type: 'taylor' # plot type
+    fig_kwargs: #Opt to define figure options
+      figsize: [8,8] # figure size if multiple plots
+    default_plot_kwargs: # Opt to define defaults for all plots. Model kwargs overwrite these.
+      linewidth: 2.0
+      markersize: 10.
+    text_kwargs: #Opt
+      fontsize: 16.
+    domain_type: ['all','epa_region'] #List of domain types: 'all' or any domain in obs file. (e.g., airnow: epa_region, state_name, siteid, etc.) 
+    domain_name: ['CONUS','R1'] #List of domain names. If domain_type = all domain_name is used in plot title.
+    data: ['airnow_cmaq_oper','airnow_cmaq_expt','airnow_wrfchem_v4.0'] # make this a list of pairs in obs_model where the obs is the obs label and model is the model_label
+    data_proc:
+      rem_obs_nan: True # True: Remove all points where model or obs variable is NaN. False: Remove only points where model variable is NaN.
+      set_axis: True #If select True, add ty_scale for each variable in obs.
+  plot_grp3:
+    type: 'spatial_bias' # plot type
+    fig_kwargs: #For all spatial plots, specify map_kwargs here too.
+      states: True
+      figsize: [10, 5] # figure size 
+    text_kwargs: #Opt
+      fontsize: 16.
+    domain_type: ['all','epa_region'] #List of domain types: 'all' or any domain in obs file. (e.g., airnow: epa_region, state_name, siteid, etc.) 
+    domain_name: ['CONUS','R1'] #List of domain names. If domain_type = all domain_name is used in plot title.
+    data: ['airnow_cmaq_oper','airnow_cmaq_expt','airnow_wrfchem_v4.0'] # make this a list of pairs in obs_model where the obs is the obs label and model is the model_label
+    data_proc:
+      rem_obs_nan: True # True: Remove all points where model or obs variable is NaN. False: Remove only points where model variable is NaN.
+      set_axis: True #If select True, add vdiff_plot for each variable in obs.
+  plot_grp4:
+    type: 'spatial_overlay' # plot type
+    fig_kwargs: #For all spatial plots, specify map_kwargs here too.
+      states: True
+      figsize: [10, 5] # figure size 
+    text_kwargs: #Opt
+      fontsize: 16.
+    domain_type: ['all','epa_region'] #List of domain types: 'all' or any domain in obs file. (e.g., airnow: epa_region, state_name, siteid, etc.) 
+    domain_name: ['CONUS','R1'] #List of domain names. If domain_type = all domain_name is used in plot title.
+    data: ['airnow_cmaq_oper','airnow_cmaq_expt','airnow_wrfchem_v4.0'] # make this a list of pairs in obs_model where the obs is the obs label and model is the model_label
+    data_proc:
+      rem_obs_nan: True # True: Remove all points where model or obs variable is NaN. False: Remove only points where model variable is NaN.
+      set_axis: True #If select True, add vmin_plot and vmax_plot for each variable in obs.
+  plot_grp5:
+    type: 'boxplot' # plot type
+    fig_kwargs: #Opt to define figure options
+      figsize: [8, 6] # figure size 
+    text_kwargs: #Opt
+      fontsize: 20.
+    domain_type: ['all','epa_region'] #List of domain types: 'all' or any domain in obs file. (e.g., airnow: epa_region, state_name, siteid, etc.) 
+    domain_name: ['CONUS','R1'] #List of domain names. If domain_type = all domain_name is used in plot title.
+    data: ['airnow_cmaq_oper','airnow_cmaq_expt','airnow_wrfchem_v4.0'] # make this a list of pairs in obs_model where the obs is the obs label and model is the model_label
+    data_proc:
+      rem_obs_nan: True # True: Remove all points where model or obs variable is NaN. False: Remove only points where model variable is NaN.
+      set_axis: False #If select True, add vmin_plot and vmax_plot for each variable in obs.
+stats:
+  rmse: True
+  mse: True
+  ioa: True 
\ No newline at end of file
diff --git a/examples/yaml/control_omps_nm-raqms.yaml b/examples/yaml/control_omps_nm-raqms.yaml
new file mode 100644
index 00000000..f772460b
--- /dev/null
+++ b/examples/yaml/control_omps_nm-raqms.yaml
@@ -0,0 +1,85 @@
+analysis:
+  start_time: '2019-08-01-00:00:00' #UTC
+  end_time: '2019-09-01-00:00:00' #UTC
+  time_interval: '5D'
+  debug: True
+  output_dir: /ships19/aqda/mbruckner/monet_plots
+  save:
+      paired:
+          method: 'netcdf'
+          prefix: 'firex_omps'
+          data: 'all'
+  read:
+      paired:
+          method: 'netcdf'
+          filenames: {'omps_nm_raqms':['firex_omps_201908*_20190*_omps_nm_raqms.nc4']}
+model:
+  raqms: # model label
+    files: /ships19/aqda/mbruckner/MELODIES-MONET-1/examples/jupyter_notebooks/raqms-files.txt
+    mod_type: 'raqms'
+    apply_ak: True # for satellite comparison, applies averaging kernels/apriori when true. Default to False
+    radius_of_influence: 120000 #meters
+    variables: #Opt
+      o3vmr: # specifying to switch units to ppbv
+        need: True
+    mapping: #model species name : obs species name
+      omps_nm: 
+        o3vmr: ozone_column #The mapping tables need to contain the same species for all models.
+    plot_kwargs: #Opt 
+      color: 'purple'
+      marker: '+'
+      linestyle: 'dotted'
+obs:
+  omps_nm: # obs label
+    filename: /ships19/aqda/mbruckner/OMPS-NPP/O3-daily/2019/nadir_mapper/OMPS-NPP_NMTO3-L2_v2.1_2019m08*t*.h5
+    obs_type: sat_swath_clm
+    variables: #Opt 
+  
+      
+plots:
+  plot_grp1:
+    type: 'timeseries' # plot type
+    fig_kwargs: #Opt to define figure options
+      figsize: [12,6] # figure size if multiple plots
+    default_plot_kwargs: # Opt to define defaults for all plots. Model kwargs overwrite these.
+      linewidth: 2.0
+      markersize: 10.
+    text_kwargs: #Opt
+      fontsize: 18.
+    domain_type: ['all'] #List of domain types: 'all' or any domain in obs file. (e.g., airnow: epa_region, state_name, siteid, etc.) 
+    domain_name: ['Global'] #List of domain names. If domain_type = all domain_name is used in plot title.
+    data: ['omps_nm_raqms'] # make this a list of pairs in obs_model where the obs is the obs label and model is the model_label
+    data_proc:
+      rem_obs_nan: True # True: Remove all points where model or obs variable is NaN. False: Remove only points where model variable is NaN.
+      ts_select_time: 'time' #Time used for avg and plotting: Options: 'time' for UTC or 'time_local'
+      ts_avg_window: 'min' # Options: None for no averaging or list pandas resample rule (e.g., 'H', 'D')
+      set_axis: False #If select True, add vmin_plot and vmax_plot for each variable in obs.
+  plot_grp2:
+    type: 'taylor' # plot type
+    fig_kwargs: #Opt to define figure options
+      figsize: [8,8] # figure size if multiple plots
+    default_plot_kwargs: # Opt to define defaults for all plots. Model kwargs overwrite these.
+      linewidth: 2.0
+      markersize: 10.
+    text_kwargs: #Opt
+      fontsize: 16.
+    domain_type: ['all'] #List of domain types: 'all' or any domain in obs file. (e.g., airnow: epa_region, state_name, siteid, etc.) 
+    domain_name: ['Global'] # of domain names. If domain_type = all domain_name is used in plot title.
+    data: ['omps_nm_raqms'] # make this a list of pairs in obs_model where the obs is the obs label and model is the model_label
+    data_proc:
+      rem_obs_nan: True # True: Remove all points where model or obs variable is NaN. False: Remove only points where model variable is NaN.
+      set_axis: True #If select True, add ty_scale for each variable in obs.
+  plot_grp3:
+    type: 'gridded_spatial_bias' #'gridded_spatial_bias' # plot type
+    fig_kwargs: #For all spatial plots, specify map_kwargs here too.
+      states: True
+      figsize: [10, 5] # figure size 
+    text_kwargs: #Opt
+      fontsize: 16.
+      #label: '$\\Delta$ DU'
+    domain_type: ['all'] #List of domain types: 'all' or any domain in obs file. (e.g., airnow: epa_region, state_name, siteid, etc.) 
+    domain_name: ['Global'] #List of domain names. If domain_type = all domain_name is used in plot title.
+    data: ['omps_nm_raqms'] # make this a list of pairs in obs_model where the obs is the obs label and model is the model_label
+    data_proc:
+      rem_obs_nan: True # True: Remove all points where model or obs variable is NaN. False: Remove only points where model variable is NaN.
+      set_axis: True #If select True, add vdiff_plot for each variable in obs.
\ No newline at end of file
diff --git a/examples/yaml/control_rrfs_cmaq_airnow_norm.yaml b/examples/yaml/control_rrfs_cmaq_airnow_norm.yaml
index 08787cdd..29c25212 100644
--- a/examples/yaml/control_rrfs_cmaq_airnow_norm.yaml
+++ b/examples/yaml/control_rrfs_cmaq_airnow_norm.yaml
@@ -167,7 +167,7 @@ plots:
       #rem_obs_by_nan_pct: {'group_var': 'siteid','pct_cutoff': 25,'times':'hourly'} # Groups by group_var, then removes all instances of groupvar where obs variable is > pct_cutoff % nan values
       rem_obs_nan: True # True: Remove all points where model or obs variable is NaN. False: Remove only points where model variable is NaN.
       ts_select_time: 'time_local' #Time used for avg and plotting: Options: 'time' for UTC or 'time_local'
-      ts_avg_window: 'H' # Options: None for no averaging or list pandas resample rule (e.g., 'H', 'D')
+      ts_avg_window: 'H' # pandas resample rule (e.g., 'H', 'D'). No averaging is done if ts_avg_window is null or not specified.
       set_axis: False #If select True, add vmin_plot and vmax_plot for each variable in obs.
   plot_grp2:
     type: 'taylor' # plot type
diff --git a/examples/yaml/control_rrfs_cmaq_airnow_reg.yaml b/examples/yaml/control_rrfs_cmaq_airnow_reg.yaml
index 6a68a3a1..e5185a5c 100644
--- a/examples/yaml/control_rrfs_cmaq_airnow_reg.yaml
+++ b/examples/yaml/control_rrfs_cmaq_airnow_reg.yaml
@@ -167,7 +167,7 @@ plots:
       #rem_obs_by_nan_pct: {'group_var': 'siteid','pct_cutoff': 25,'times':'hourly'} # Groups by group_var, then removes all instances of groupvar where obs variable is > pct_cutoff % nan values
       rem_obs_nan: True # True: Remove all points where model or obs variable is NaN. False: Remove only points where model variable is NaN.
       ts_select_time: 'time_local' #Time used for avg and plotting: Options: 'time' for UTC or 'time_local'
-      ts_avg_window: 'D' # Options: None for no averaging or list pandas resample rule (e.g., 'H', 'D')
+      ts_avg_window: 'D' # pandas resample rule (e.g., 'H', 'D'). No averaging is done if ts_avg_window is null or not specified.
       set_axis: False #If select True, add vmin_plot and vmax_plot for each variable in obs.
   plot_grp2:
     type: 'taylor' # plot type
diff --git a/examples/yaml/control_tropomi_l2_no2.yaml b/examples/yaml/control_tropomi_l2_no2.yaml
new file mode 100644
index 00000000..9b01eeea
--- /dev/null
+++ b/examples/yaml/control_tropomi_l2_no2.yaml
@@ -0,0 +1,23 @@
+analysis:
+  start_time: '2022-04-30'
+  end_time:   '2022-05-01'
+  debug: True
+
+obs:
+  tropomi_l2_no2:
+    debug: True
+    filename: /Users/mengli/Work/melodies-monet/obsdata/tropomi_no2/*
+    obs_type: sat_swath_clm
+    variables:
+        qa_value:
+            quality_flag_min: 0.7
+        nitrogendioxide_tropospheric_column:       
+            scale: 6.022141e+19 # unit convert form mol_perm2to molec_percm2,6.022141e+19
+            fillvalue: 9.96921e+36
+        #averaging_kernel: None
+        #air_mass_factor_troposphere: None
+        #tm5_tropopause_layer_index: None
+        #tm5_constant_a: None
+        #tm5_constant_b: None
+      
+        
diff --git a/examples/yaml/control_wrfchem.yaml b/examples/yaml/control_wrfchem.yaml
index d796a40d..162e8090 100644
--- a/examples/yaml/control_wrfchem.yaml
+++ b/examples/yaml/control_wrfchem.yaml
@@ -117,7 +117,7 @@ plots:
       #rem_obs_by_nan_pct: {'group_var': 'siteid','pct_cutoff': 25,'times':'hourly'} # Groups by group_var, then removes all instances of groupvar where obs variable is > pct_cutoff % nan values
       rem_obs_nan: True # True: Remove all points where model or obs variable is NaN. False: Remove only points where model variable is NaN.
       ts_select_time: 'time_local' #Time used for avg and plotting: Options: 'time' for UTC or 'time_local'
-      ts_avg_window: 'H' # Options: None for no averaging or list pandas resample rule (e.g., 'H', 'D')
+      ts_avg_window: 'H' # pandas resample rule (e.g., 'H', 'D'). No averaging is done if ts_avg_window is null or not specified.
       set_axis: False #If select True, add vmin_plot and vmax_plot for each variable in obs.
   plot_grp2:
     type: 'taylor' # plot type
diff --git a/melodies_monet/_cli.py b/melodies_monet/_cli.py
index af0b7656..b4f49904 100644
--- a/melodies_monet/_cli.py
+++ b/melodies_monet/_cli.py
@@ -148,7 +148,14 @@ def run(
         an.open_obs()
 
     with _timer("Pairing"):
-        an.pair_data()
+        if an.read is not None:
+            an.read_analysis()
+        else:
+            an.pair_data()
+
+    if an.save is not None:
+        with _timer("Saving paired datasets"):
+            an.save_analysis()
 
     if an.control_dict.get("plots") is not None:
         with _timer("Plotting and saving the figures"), _ignore_pandas_numeric_only_futurewarning():
diff --git a/melodies_monet/driver.py b/melodies_monet/driver.py
index ada9b1f5..cc3f1ce8 100644
--- a/melodies_monet/driver.py
+++ b/melodies_monet/driver.py
@@ -12,7 +12,7 @@
 import numpy as np
 import datetime
 
-# from util import write_ncf
+from .util import write_util
 
 __all__ = (
     "pair",
@@ -117,6 +117,7 @@ def __init__(self):
         self.obj = None
         """The data object (:class:`pandas.DataFrame` or :class:`xarray.Dataset`)."""
         self.type = 'pt_src'
+        self.sat_type = None
         self.data_proc = None
         self.variable_dict = None
 
@@ -133,7 +134,7 @@ def __repr__(self):
             ")"
         )
 
-    def open_obs(self, time_interval=None):
+    def open_obs(self, time_interval=None, control_dict=None):
         """Open the observational data, store data in observation pair,
         and apply mask and scaling.
 
@@ -148,41 +149,117 @@ def open_obs(self, time_interval=None):
         """
         from glob import glob
         from numpy import sort
+        
         from . import tutorial
+        from .util import analysis_util
+        from .util import read_grid_util
+
+        time_chunking_with_gridded_data \
+            = 'time_chunking_with_gridded_data' in control_dict['analysis'].keys() \
+                and control_dict['analysis']['time_chunking_with_gridded_data']
+
+        if time_chunking_with_gridded_data:
+            date_str = time_interval[0].strftime('%Y-%m-%b-%d-%j')
+            print('obs time chunk %s' % date_str)
+            obs_vars = analysis_util.get_obs_vars(control_dict)
+            print(obs_vars)
+            obs_datasets, filenames = read_grid_util.read_grid_obs(
+                control_dict, obs_vars, date_str, obs=self.obs)
+            print(filenames)
+            self.obj = obs_datasets[self.obs]
 
-        if self.file.startswith("example:"):
-            example_id = ":".join(s.strip() for s in self.file.split(":")[1:])
-            files = [tutorial.fetch_example(example_id)]
         else:
-            files = sort(glob(self.file))
+            if self.file.startswith("example:"):
+                example_id = ":".join(s.strip() for s in self.file.split(":")[1:])
+                files = [tutorial.fetch_example(example_id)]
+            else:
+                files = sort(glob(self.file))
 
-        assert len(files) >= 1, "need at least one"
+            assert len(files) >= 1, "need at least one"
 
-        _, extension = os.path.splitext(files[0])
-        try:
-            if extension in {'.nc', '.ncf', '.netcdf', '.nc4'}:
-                if len(files) > 1:
-                    self.obj = xr.open_mfdataset(files)
+            _, extension = os.path.splitext(files[0])
+            try:
+                if extension in {'.nc', '.ncf', '.netcdf', '.nc4'}:
+                    if len(files) > 1:
+                        self.obj = xr.open_mfdataset(files)
+                    else:
+                        self.obj = xr.open_dataset(files[0])
+                elif extension in ['.ict', '.icarrt']:
+                    assert len(files) == 1, "monetio.icarrt.add_data can only read one file"
+                    self.obj = mio.icarrt.add_data(files[0])
                 else:
-                    self.obj = xr.open_dataset(files[0])
-            elif extension in ['.ict', '.icarrt']:
-                assert len(files) == 1, "monetio.icarrt.add_data can only read one file"
-                self.obj = mio.icarrt.add_data(files[0])
-            else:
-                raise ValueError(f'extension {extension!r} currently unsupported')
-        except Exception as e:
-            print('something happened opening file:', e)
-            return
+                    raise ValueError(f'extension {extension!r} currently unsupported')
+            except Exception as e:
+                print('something happened opening file:', e)
+                return
 
         self.mask_and_scale()  # mask and scale values from the control values
         self.filter_obs()
+
+    def open_sat_obs(self,time_interval=None):
+        """Methods to opens satellite data observations. 
+        Uses in-house python code to open and load observations.
+        Alternatively may use the satpy reader.
+        Fills the object class associated with the equivalent label (self.label) with satellite observation
+        dataset read in from the associated file (self.file) by the satellite file reader
+
+        Parameters
+        __________
+        time_interval (optional, default None) : [pandas.Timestamp, pandas.Timestamp]
+            If not None, restrict obs to datetime range spanned by time interval [start, end].
+
+        Returns
+        -------
+        None
+        """
+        from .util import time_interval_subset as tsub
+        
+        try:
+            if self.sat_type == 'omps_l3':
+                print('Reading OMPS L3')
+                self.obj = mio.sat._omps_l3_mm.read_OMPS_l3(self.file)
+            elif self.sat_type == 'omps_nm':
+                print('Reading OMPS_NM')
+                if time_interval is not None:
+                    flst = tsub.subset_OMPS_l2(self.file,time_interval)
+                else: flst = self.file
+
+                self.obj = mio.sat._omps_nadir_mm.read_OMPS_nm(flst)
+                
+                # couple of changes to move to reader
+                self.obj = self.obj.swap_dims({'x':'time'}) # indexing needs
+                self.obj = self.obj.sortby('time') # enforce time in order. 
+                # restrict observation data to time_interval if using
+                # additional development to deal with files crossing intervals needed (eg situtations where orbit start at 23hrs, ends next day).
+                if time_interval is not None:
+                    self.obj = self.obj.sel(time=slice(time_interval[0],time_interval[-1]))
+                    
+            elif self.sat_type == 'mopitt_l3':
+                print('Reading MOPITT')
+                self.obj = mio.sat._mopitt_l3_mm.read_mopittdataset(self.file, 'column')
+            elif self.sat_type == 'modis_l2':
+                from monetio import modis_l2
+                print('Reading MODIS L2')
+                self.obj = modis_l2.read_mfdataset(
+                    self.file, self.variable_dict, debug=self.debug)
+            elif self.sat_type == 'tropomi_l2_no2':
+                from monetio import tropomi_l2_no2
+                print('Reading TROPOMI L2 NO2')
+                self.obj = tropomi_l2_no2.read_trpdataset(
+                    self.file, self.variable_dict, debug=self.debug)
+            else:
+                print('file reader not implemented for {} observation'.format(self.sat_type))
+                raise ValueError
+        except ValueError as e:
+            print('something happened opening file:', e)
+            return
         
     def filter_obs(self):
         """Filter observations based on filter_dict.
         
         Returns
         -------
-        None
+            None
         """
         if self.data_proc is not None:
             if 'filter_dict' in self.data_proc:
@@ -208,7 +285,7 @@ def filter_obs(self):
                         self.obj = self.obj.where(self.obj[column] != filter_vals,drop=True)
                     else:
                         raise ValueError(f'Filter operation {filter_op!r} is not supported')
-
+        
     def mask_and_scale(self):
         """Mask and scale observations, including unit conversions and setting
         detection limits.
@@ -251,8 +328,10 @@ def obs_to_df(self):
         -------
         None
         """
-        self.obj = self.obj.to_dataframe().reset_index().drop(['x', 'y'], axis=1)
-
+        try:
+            self.obj = self.obj.to_dataframe().reset_index().drop(['x', 'y'], axis=1)
+        except KeyError:
+            self.obj = self.obj.to_dataframe().reset_index().drop(['x'], axis=1)
 
 class model:
     """The model class.
@@ -263,6 +342,7 @@ class model:
     def __init__(self):
         """Initialize a :class:`model` object."""
         self.model = None
+        self.apply_ak = False
         self.radius_of_influence = None
         self.mod_kwargs = {}
         self.file_str = None
@@ -313,7 +393,13 @@ def glob_files(self):
             self.files = [tutorial.fetch_example(example_id)]
         else:
             self.files = sort(glob(self.file_str))
-            
+ 
+        # add option to read list of files from text file
+        _, extension = os.path.splitext(self.file_str)
+        if extension.lower() == '.txt':
+            with open(self.file_str,'r') as f:
+                self.files = f.read().split()
+
         if self.file_vert_str is not None:
             self.files_vert = sort(glob(self.file_vert_str))
         if self.file_surf_str is not None:
@@ -321,7 +407,7 @@ def glob_files(self):
         if self.file_pm25_str is not None:
             self.files_pm25 = sort(glob(self.file_pm25_str))
 
-    def open_model_files(self, time_interval=None):
+    def open_model_files(self, time_interval=None, control_dict=None):
         """Open the model files, store data in :class:`model` instance attributes,
         and apply mask and scaling.
         
@@ -339,6 +425,16 @@ def open_model_files(self, time_interval=None):
         -------
         None
         """
+        from .util import time_interval_subset as tsub
+        from .util import analysis_util
+        from .util import read_grid_util
+        from .util import regrid_util
+
+        print(self.model.lower())
+
+        time_chunking_with_gridded_data \
+            = 'time_chunking_with_gridded_data' in control_dict['analysis'].keys() \
+                and control_dict['analysis']['time_chunking_with_gridded_data']
 
         self.glob_files()
         # Calculate species to input into MONET, so works for all mechanisms in wrfchem
@@ -347,59 +443,68 @@ def open_model_files(self, time_interval=None):
         for obs_map in self.mapping:
             list_input_var = list_input_var + list(set(self.mapping[obs_map].keys()) - set(list_input_var))
         #Only certain models need this option for speeding up i/o.
-        if 'cmaq' in self.model.lower():
-            print('**** Reading CMAQ model output...')
-            self.mod_kwargs.update({'var_list' : list_input_var})
-            if self.files_vert is not None:
-                self.mod_kwargs.update({'fname_vert' : self.files_vert})
-            if self.files_surf is not None:
-                self.mod_kwargs.update({'fname_surf' : self.files_surf})
-            if len(self.files) > 1:
-                self.mod_kwargs.update({'concatenate_forecasts' : True})
-            self.obj = mio.models._cmaq_mm.open_mfdataset(self.files,**self.mod_kwargs)
-        elif 'wrfchem' in self.model.lower():
-            print('**** Reading WRF-Chem model output...')
-            self.mod_kwargs.update({'var_list' : list_input_var})
-            self.obj = mio.models._wrfchem_mm.open_mfdataset(self.files,**self.mod_kwargs)
-        elif 'rrfs' in self.model.lower():
-            print('**** Reading RRFS-CMAQ model output...')
-            if self.files_pm25 is not None:
-                self.mod_kwargs.update({'fname_pm25' : self.files_pm25})
-            self.mod_kwargs.update({'var_list' : list_input_var})
-            self.obj = mio.models._rrfs_cmaq_mm.open_mfdataset(self.files,**self.mod_kwargs)
-        elif 'gsdchem' in self.model.lower():
-            print('**** Reading GSD-Chem model output...')
-            if len(self.files) > 1:
-                self.obj = mio.fv3chem.open_mfdataset(self.files,**self.mod_kwargs)
-            else:
-                self.obj = mio.fv3chem.open_dataset(self.files,**self.mod_kwargs)
-        elif 'cesm_fv' in self.model.lower():
-            print('**** Reading CESM FV model output...')
-            self.mod_kwargs.update({'var_list' : list_input_var})
-            self.obj = mio.models._cesm_fv_mm.open_mfdataset(self.files,**self.mod_kwargs)
-        # CAM-chem-SE grid or MUSICAv0
-        elif 'cesm_se' in self.model.lower(): 
-            print('**** Reading CESM SE model output...')
-            self.mod_kwargs.update({'var_list' : list_input_var})
-            if self.scrip_file.startswith("example:"):
-                from . import tutorial
-                example_id = ":".join(s.strip() for s in self.scrip_file.split(":")[1:])
-                self.scrip_file = tutorial.fetch_example(example_id)
-            self.mod_kwargs.update({'scrip_file' : self.scrip_file})            
-            self.obj = mio.models._cesm_se_mm.open_mfdataset(self.files,**self.mod_kwargs)
-            #self.obj, self.obj_scrip = read_cesm_se.open_mfdataset(self.files,**self.mod_kwargs)
-            #self.obj.monet.scrip = self.obj_scrip
-        elif 'raqms' in self.model.lower():
-            if len(self.files) > 1:
-                self.obj = mio.raqms.open_mfdataset(self.files,**self.mod_kwargs)
-            else:
-                self.obj = mio.raqms.open_dataset(self.files,**self.mod_kwargs)
+
+        if time_chunking_with_gridded_data:
+            date_str = time_interval[0].strftime('%Y-%m-%b-%d-%j')
+            print('model time chunk %s' % date_str)
+            model_datasets, filenames = read_grid_util.read_grid_models(
+                control_dict, date_str, model=self.label)
+            print(filenames)
+            self.obj = model_datasets[self.label]
         else:
-            print('**** Reading Unspecified model output. Take Caution...')
-            if len(self.files) > 1:
-                self.obj = xr.open_mfdataset(self.files,**self.mod_kwargs)
+            if 'cmaq' in self.model.lower():
+                print('**** Reading CMAQ model output...')
+                self.mod_kwargs.update({'var_list' : list_input_var})
+                if self.files_vert is not None:
+                    self.mod_kwargs.update({'fname_vert' : self.files_vert})
+                if self.files_surf is not None:
+                    self.mod_kwargs.update({'fname_surf' : self.files_surf})
+                if len(self.files) > 1:
+                    self.mod_kwargs.update({'concatenate_forecasts' : True})
+                self.obj = mio.models._cmaq_mm.open_mfdataset(self.files,**self.mod_kwargs)
+            elif 'wrfchem' in self.model.lower():
+                print('**** Reading WRF-Chem model output...')
+                self.mod_kwargs.update({'var_list' : list_input_var})
+                self.obj = mio.models._wrfchem_mm.open_mfdataset(self.files,**self.mod_kwargs)
+            elif 'rrfs' in self.model.lower():
+                print('**** Reading RRFS-CMAQ model output...')
+                if self.files_pm25 is not None:
+                    self.mod_kwargs.update({'fname_pm25' : self.files_pm25})
+                self.mod_kwargs.update({'var_list' : list_input_var})
+                self.obj = mio.models._rrfs_cmaq_mm.open_mfdataset(self.files,**self.mod_kwargs)
+            elif 'gsdchem' in self.model.lower():
+                print('**** Reading GSD-Chem model output...')
+                if len(self.files) > 1:
+                    self.obj = mio.fv3chem.open_mfdataset(self.files,**self.mod_kwargs)
+                else:
+                    self.obj = mio.fv3chem.open_dataset(self.files,**self.mod_kwargs)
+            elif 'cesm_fv' in self.model.lower():
+                print('**** Reading CESM FV model output...')
+                self.mod_kwargs.update({'var_list' : list_input_var})
+                self.obj = mio.models._cesm_fv_mm.open_mfdataset(self.files,**self.mod_kwargs)
+            # CAM-chem-SE grid or MUSICAv0
+            elif 'cesm_se' in self.model.lower(): 
+                print('**** Reading CESM SE model output...')
+                self.mod_kwargs.update({'var_list' : list_input_var})
+                if self.scrip_file.startswith("example:"):
+                    from . import tutorial
+                    example_id = ":".join(s.strip() for s in self.scrip_file.split(":")[1:])
+                    self.scrip_file = tutorial.fetch_example(example_id)
+                self.mod_kwargs.update({'scrip_file' : self.scrip_file})            
+                self.obj = mio.models._cesm_se_mm.open_mfdataset(self.files,**self.mod_kwargs)
+                #self.obj, self.obj_scrip = read_cesm_se.open_mfdataset(self.files,**self.mod_kwargs)
+                #self.obj.monet.scrip = self.obj_scrip
+            elif 'raqms' in self.model.lower():
+                if len(self.files) > 1:
+                    self.obj = mio.raqms.open_mfdataset(self.files,**self.mod_kwargs)
+                else:
+                    self.obj = mio.raqms.open_dataset(self.files,**self.mod_kwargs)
             else:
-                self.obj = xr.open_dataset(self.files[0],**self.mod_kwargs)
+                print('**** Reading Unspecified model output. Take Caution...')
+                if len(self.files) > 1:
+                    self.obj = xr.open_mfdataset(self.files,**self.mod_kwargs)
+                else:
+                    self.obj = xr.open_dataset(self.files[0],**self.mod_kwargs)
         self.mask_and_scale()
 
     def mask_and_scale(self):
@@ -427,6 +532,10 @@ def mask_and_scale(self):
                             self.obj[v].data += scale
                         elif d['unit_scale_method'] == '-':
                             self.obj[v].data += -1 * scale
+                    if self.obj[v].units == 'ppv':
+                        print('changing units for {}'.format(v))
+                        self.obj[v].values *= 1e9
+                        self.obj[v].attrs['units'] = 'ppbv'        
 
 class analysis:
     """The analysis class.
@@ -458,6 +567,11 @@ def __init__(self):
         self.debug = False
         self.save = None
         self.read = None
+        self.time_chunking_with_gridded_data = False  # Default to False
+        self.regrid = False  # Default to False
+        self.target_grid = None
+        self.obs_regridders = None
+        self.model_regridders = None
 
     def __repr__(self):
         return (
@@ -479,7 +593,6 @@ def __repr__(self):
             f"    read={self.read!r},\n"
             ")"
         )
-
     def read_control(self, control=None):
         """Read the input yaml file,
         updating various :class:`analysis` instance attributes.
@@ -492,7 +605,8 @@ def read_control(self, control=None):
 
         Returns
         -------
-        None
+        type
+            Reads the contents of the yaml control file into a dictionary associated with the analysis class.
         """
         import yaml
 
@@ -528,6 +642,14 @@ def read_control(self, control=None):
         if 'read' in self.control_dict['analysis'].keys():
             self.read = self.control_dict['analysis']['read']
 
+        # set time_chunking_with_gridded_data option, regrid option, and target_grid
+        if 'time_chunking_with_gridded_data' in self.control_dict['analysis'].keys():
+            self.time_chunking_with_gridded_data = self.control_dict['analysis']['time_chunking_with_gridded_data']
+        if 'regrid' in self.control_dict['analysis'].keys():
+            self.regrid = self.control_dict['analysis']['regrid']
+        if 'target_grid' in self.control_dict['analysis'].keys():
+            self.target_grid = self.control_dict['analysis']['target_grid']
+
         # generate time intervals for time chunking
         if 'time_interval' in self.control_dict['analysis'].keys():
             time_stamps = pd.date_range(
@@ -541,7 +663,7 @@ def read_control(self, control=None):
             self.time_intervals \
                 = [[time_stamps[n], time_stamps[n+1]]
                     for n in range(len(time_stamps)-1)]
-
+        
         # Enable Dask progress bars? (default: false)
         enable_dask_progress_bars = self.control_dict["analysis"].get(
             "enable_dask_progress_bars", False)
@@ -600,8 +722,26 @@ def read_analysis(self):
                     read_saved_data(analysis=self,filenames=self.read[attr]['filenames'], method='pkl', attr=attr)
                 elif self.read[attr]['method']=='netcdf':
                     read_saved_data(analysis=self,filenames=self.read[attr]['filenames'], method='netcdf', attr=attr)
+                if attr == 'paired':
+                    # initialize model/obs attributes, since needed for plotting and stats
+                    if not self.models:
+                        self.open_models(load_files=False)
+                    if not self.obs:
+                        self.open_obs(load_files=False)
+
+    def setup_regridders(self):
+        """Create an obs xesmf.Regridder from base and target grids specified in the control_dict
+
+        Returns
+        -------
+        None
+        """
+        from .util import regrid_util
+        if self.regrid:
+            self.obs_regridders = regrid_util.setup_regridder(self.control_dict, config_group='obs')
+            self.model_regridders = regrid_util.setup_regridder(self.control_dict, config_group='model')
 
-    def open_models(self, time_interval=None):
+    def open_models(self, time_interval=None,load_files=True):
         """Open all models listed in the input yaml file and create a :class:`model` 
         object for each of them, populating the :attr:`models` dict.
 
@@ -609,7 +749,8 @@ def open_models(self, time_interval=None):
         __________
         time_interval (optional, default None) : [pandas.Timestamp, pandas.Timestamp]
             If not None, restrict models to datetime range spanned by time interval [start, end].
-
+        load_files (optional, default True): boolean
+            If False, only populate :attr: dict with yaml file parameters and do not open model files. 
         Returns
         -------
         None
@@ -622,10 +763,13 @@ def open_models(self, time_interval=None):
                 # this is the model type (ie cmaq, rapchem, gsdchem etc)
                 m.model = self.control_dict['model'][mod]['mod_type']
                 # set the model label in the dictionary and model class instance
+                if "apply_ak" in self.control_dict['model'][mod].keys():
+                    m.apply_ak = self.control_dict['model'][mod]['apply_ak']
                 if 'radius_of_influence' in self.control_dict['model'][mod].keys():
                     m.radius_of_influence = self.control_dict['model'][mod]['radius_of_influence']
                 else:
                     m.radius_of_influence = 1e6
+                        
                 if 'mod_kwargs' in self.control_dict['model'][mod].keys():
                     m.mod_kwargs = self.control_dict['model'][mod]['mod_kwargs']    
                 m.label = mod
@@ -644,6 +788,7 @@ def open_models(self, time_interval=None):
                 # create mapping
                 m.mapping = self.control_dict['model'][mod]['mapping']
                 # add variable dict
+
                 if 'variables' in self.control_dict['model'][mod].keys():
                     m.variable_dict = self.control_dict['model'][mod]['variables']
                 if 'plot_kwargs' in self.control_dict['model'][mod].keys():
@@ -682,10 +827,11 @@ def open_models(self, time_interval=None):
                             m.proj = ccrs.Projection(proj_in)
 
                 # open the model
-                m.open_model_files(time_interval=time_interval)
+                if load_files:
+                    m.open_model_files(time_interval=time_interval, control_dict=self.control_dict)
                 self.models[m.label] = m
 
-    def open_obs(self, time_interval=None):
+    def open_obs(self, time_interval=None, load_files=True):
         """Open all observations listed in the input yaml file and create an 
         :class:`observation` instance for each of them,
         populating the :attr:`obs` dict.
@@ -694,12 +840,17 @@ def open_obs(self, time_interval=None):
         __________
         time_interval (optional, default None) : [pandas.Timestamp, pandas.Timestamp]
             If not None, restrict obs to datetime range spanned by time interval [start, end].
-
-
+        load_files (optional, default True): boolean
+            If False, only populate :attr: dict with yaml file parameters and do not open obs files. 
+            
         Returns
         -------
         None
         """
+        from .util import analysis_util
+        from .util import read_grid_util
+        from .util import regrid_util
+
         if 'obs' in self.control_dict:
             for obs in self.control_dict['obs']:
                 o = observation()
@@ -710,11 +861,21 @@ def open_obs(self, time_interval=None):
                     o.data_proc = self.control_dict['obs'][obs]['data_proc']
                 o.file = os.path.expandvars(
                     self.control_dict['obs'][obs]['filename'])
+                if 'debug' in self.control_dict['obs'][obs].keys():
+                    o.debug = self.control_dict['obs'][obs]['debug']
                 if 'variables' in self.control_dict['obs'][obs].keys():
                     o.variable_dict = self.control_dict['obs'][obs]['variables']
-                o.open_obs(time_interval=time_interval)
+                if 'sat_type' in self.control_dict['obs'][obs].keys():
+                    o.sat_type = self.control_dict['obs'][obs]['sat_type']
+                if load_files:
+                    if o.obs_type in ['sat_swath_sfc', 'sat_swath_clm', 'sat_grid_sfc',\
+                                        'sat_grid_clm', 'sat_swath_prof']:
+                        o.open_sat_obs(time_interval=time_interval)
+                    else:
+                        o.open_obs(time_interval=time_interval, control_dict=self.control_dict)
                 self.obs[o.label] = o
 
+
     def pair_data(self, time_interval=None):
         """Pair all observations and models in the analysis class
         (i.e., those listed in the input yaml file) together,
@@ -785,7 +946,52 @@ def pair_data(self, time_interval=None):
                     p.obj = p.fix_paired_xarray(dset=p.obj)
                     # write_util.write_ncf(p.obj,p.filename) # write out to file
                 # TODO: add other network types / data types where (ie flight, satellite etc)
-
+                # if sat_swath_clm (satellite l2 column products)
+                elif obs.obs_type.lower() == 'sat_swath_clm':
+                    
+                    if obs.label == 'omps_nm':
+                        
+                        from .util import satellite_utilities as sutil
+                        
+                        #necessary observation index things 
+                        #the along track coordinate dim sometimes needs to be time and other times an unassigned 'x'
+                        obs.obj = obs.obj.swap_dims({'time':'x'})
+                        if mod.apply_ak == True:
+                            model_obj = mod.obj[keys+['pres_pa_mid','surfpres_pa']]
+                            
+                            paired_data = sutil.omps_nm_pairing_apriori(model_obj,obs.obj,keys)
+                        else:
+                            model_obj = mod.obj[keys+['dp_pa']]
+                            paired_data = sutil.omps_nm_pairing(model_obj,obs.obj,keys)
+
+                        paired_data = paired_data.where((paired_data.o3vmr > 0))
+                        p = pair()
+                        p.type = obs.obs_type
+                        p.obs = obs.label
+                        p.model = mod.label
+                        p.model_vars = keys
+                        p.obs_vars = obs_vars
+                        p.obj = paired_data 
+                        label = '{}_{}'.format(p.obs,p.model)
+                        self.paired[label] = p
+                # if sat_grid_clm (satellite l3 column products)
+                elif obs.obs_type.lower() == 'sat_grid_clm':
+                    if obs.label == 'omps_l3':
+                        from .util import satellite_utilities as sutil
+                        # trim obs array to only data within analysis window
+                        obs_dat = obs.obj.sel(time=slice(self.start_time.date(),self.end_time.date())).copy()
+                        model_obsgrid = sutil.omps_l3_daily_o3_pairing(mod.obj,obs_dat,'o3vmr')
+                        # combine model and observations into paired dataset
+                        obs_dat['o3vmr'] = (['time','x','y'],model_obsgrid.sel(time=slice(self.start_time.date(),self.end_time.date())).data)
+                        p = pair()
+                        p.type = obs.obs_type
+                        p.obs = obs.label
+                        p.model = mod.label
+                        p.model_vars = keys
+                        p.obs_vars = obs_vars
+                        p.obj = obs_dat
+                        label = '{}_{}'.format(p.obs,p.model)
+                        self.paired[label] = p
     def concat_pairs(self):
         """Read and concatenate all observation and model time interval pair data,
         populating the :attr:`paired` dict.
@@ -795,7 +1001,7 @@ def concat_pairs(self):
         None
         """
         pass
-
+    
     ### TODO: Create the plotting driver (most complicated one)
     # def plotting(self):
     def plotting(self):
@@ -815,8 +1021,11 @@ def plotting(self):
         None
         """
         import matplotlib.pyplot as plt
-
-        from .plots import surfplots as splots, savefig
+        pair_keys = list(self.paired.keys())
+        if self.paired[pair_keys[0]].type.lower() == 'pt_sfc':
+            from .plots import surfplots as splots,savefig
+        else:
+            from .plots import satplots as splots,savefig
 
         # Disable figure count warning
         initial_max_fig = plt.rcParams["figure.max_open_warning"]
@@ -840,7 +1049,7 @@ def plotting(self):
             # first get the observational obs labels
             pair1 = self.paired[list(self.paired.keys())[0]]
             obs_vars = pair1.obs_vars
-
+            obs_type = pair1.type
             # loop through obs variables
             for obsvar in obs_vars:
                 # Loop also over the domain types. So can easily create several overview and zoomed in plots.
@@ -860,13 +1069,32 @@ def plotting(self):
                         # Adjust the modvar as done in pairing script, if the species name in obs and model are the same.
                         if obsvar == modvar:
                             modvar = modvar + '_new'
-
-                        # convert to dataframe
-                        pairdf_all = p.obj.to_dataframe(dim_order=["time", "x"])
-
-                        # Select only the analysis time window.
-                        pairdf_all = pairdf_all.loc[self.start_time : self.end_time]
-
+                            
+                        # for pt_sfc data, convert to pandas dataframe, format, and trim
+                        if obs_type == 'pt_sfc':
+                            # convert to dataframe
+                            pairdf_all = p.obj.to_dataframe(dim_order=["time", "x"])
+                            # Select only the analysis time window.
+                            pairdf_all = pairdf_all.loc[self.start_time : self.end_time]
+                        
+                        # keep data in xarray, fix formatting, and trim
+                        elif obs_type in ["sat_swath_sfc", "sat_swath_clm", 
+                                                                        "sat_grid_sfc", "sat_grid_clm", 
+                                                                        "sat_swath_prof"]:
+                             # convert index to time; setup for sat_swath_clm
+                            
+                            if 'time' not in p.obj.dims and obs_type == 'sat_swath_clm':
+                                
+                                pairdf_all = p.obj.swap_dims({'x':'time'})
+                            # squash lat/lon dimensions into single dimension
+                            elif obs_type == 'sat_grid_clm':
+                                pairdf_all = p.obj.stack(ll=['x','y'])
+                                pairdf_all = pairdf_all.rename_dims({'ll':'y'})
+                            else:
+                                pairdf_all = p.obj
+                            # Select only the analysis time window.
+                            pairdf_all = pairdf_all.sel(time=slice(self.start_time,self.end_time))
+                            
                         # Determine the default plotting colors.
                         if 'default_plot_kwargs' in grp_dict.keys():
                             if self.models[p.model].plot_kwargs is not None:
@@ -968,17 +1196,26 @@ def plotting(self):
                             # make list of sites meeting condition and select paired data by this by this
                             grp_select = grp_pct_nan.query(obsvar + ' < ' + str(pct_cutoff)).reset_index()
                             pairdf_all = pairdf_all.loc[pairdf_all[grp_var].isin(grp_select[grp_var].values)]
-                        
-                        # Drop NaNs
-                        if grp_dict['data_proc']['rem_obs_nan'] == True:
-                            # I removed drop=True in reset_index in order to keep 'time' as a column.
-                            pairdf = pairdf_all.reset_index().dropna(subset=[modvar, obsvar])
+
+                        # Drop NaNs if using pandas 
+                        if obs_type == 'pt_sfc':
+                            if grp_dict['data_proc']['rem_obs_nan'] == True:
+                                # I removed drop=True in reset_index in order to keep 'time' as a column.
+                                pairdf = pairdf_all.reset_index().dropna(subset=[modvar, obsvar])
+                            else:
+                                pairdf = pairdf_all.reset_index().dropna(subset=[modvar])
+                        elif obs_type in  ["sat_swath_sfc", "sat_swath_clm", 
+                                                                        "sat_grid_sfc", "sat_grid_clm", 
+                                                                        "sat_swath_prof"]: 
+                            # xarray doesn't need nan drop because its math operations seem to ignore nans
+                            pairdf = pairdf_all
                         else:
                             print('Warning: set rem_obs_nan = True for regulatory metrics') 
                             pairdf = pairdf_all.reset_index().dropna(subset=[modvar])
 
                         # JianHe: do we need provide a warning if pairdf is empty (no valid obsdata) for specific subdomain?
-                        if pairdf.empty or pairdf[obsvar].isnull().all():
+                        # MEB: pairdf.empty fails for data left in xarray format. isnull format works.
+                        if pairdf[obsvar].isnull().all():
                             print('Warning: no valid obs found for '+domain_name)
                             continue
 
@@ -1042,7 +1279,11 @@ def plotting(self):
                                 vmax = None
                             # Select time to use as index.
                             pairdf = pairdf.set_index(grp_dict['data_proc']['ts_select_time'])
-                            a_w = grp_dict['data_proc']['ts_avg_window']
+                            # Specify ts_avg_window if noted in yaml file. 
+                            if 'ts_avg_window' in grp_dict['data_proc'].keys():
+                                a_w = grp_dict['data_proc']['ts_avg_window']
+                            else:
+                                a_w = None  
                             if p_index == 0:
                                 # First plot the observations.
                                 ax = splots.make_timeseries(
@@ -1197,6 +1438,22 @@ def plotting(self):
                                 text_dict=text_dict,
                                 debug=self.debug
                             )
+                        elif plot_type.lower() == 'gridded_spatial_bias':
+                            splots.make_spatial_bias_gridded(
+                                p.obj,
+                                column_o=obsvar,
+                                label_o=p.obs,
+                                column_m=modvar,
+                                label_m=p.model,
+                                ylabel=use_ylabel,
+                                #vdiff=vdiff,
+                                outname=outname,
+                                domain_type=domain_type,
+                                domain_name=domain_name,
+                                fig_dict=fig_dict,
+                                text_dict=text_dict,
+                                debug=self.debug
+                                )    
                             del (fig_dict, plot_dict, text_dict, obs_dict, obs_plot_dict) #Clear info for next plot.
                         elif plot_type.lower() == 'spatial_bias_exceedance':
                             if cal_reg:
diff --git a/melodies_monet/plots/satplots.py b/melodies_monet/plots/satplots.py
new file mode 100644
index 00000000..a16f31a8
--- /dev/null
+++ b/melodies_monet/plots/satplots.py
@@ -0,0 +1,765 @@
+# Copyright (C) 2022 National Center for Atmospheric Research and National Oceanic and Atmospheric Administration
+# SPDX-License-Identifier: Apache-2.0
+#
+#Code to create plots for satellite observations
+# Copied from surfplots and altered to use xarray syntax instead of pandas
+
+import os
+import monetio as mio
+import monet as monet
+import seaborn as sns
+from monet.util.tools import calc_8hr_rolling_max, calc_24hr_ave
+import xarray as xr
+import pandas as pd
+import numpy as np
+import cartopy.crs as ccrs
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+from numpy import corrcoef
+sns.set_context('paper')
+from monet.plots.taylordiagram import TaylorDiagram as td
+from matplotlib.colors import ListedColormap
+from monet.util.tools import get_epa_region_bounds as get_epa_bounds 
+import math
+from ..plots import savefig
+
+def make_24hr_regulatory(df, col=None):
+    """Calculates 24-hour averages
+    
+    Parameters
+    ----------
+    df : dataframe
+        Model/obs pair of hourly data
+    col : str
+        Column label of observation variable to apply the calculation 
+    Returns
+    -------
+    dataframe
+        dataframe with applied calculation
+        
+    """
+    return calc_24hr_ave(df, col)
+
+
+def make_8hr_regulatory(df, col=None):
+    """Calculates 8-hour rolling average daily
+    
+    Parameters
+    ----------
+    df : dataframe
+        Model/obs pair of hourly data
+    col : str
+        Column label of observation variable to apply the calculation 
+    Returns
+    -------
+    dataframe
+        dataframe with applied calculation
+        
+    """
+    return calc_8hr_rolling_max(df, col, window=8)
+
+def calc_default_colors(p_index):
+    """List of default colors, lines, and markers to use if user does not 
+    specify them in the input yaml file.
+    
+    Parameters
+    ----------
+    p_index : integer
+        Number of pairs in analysis class
+    
+    Returns
+    -------
+    list
+        List of dictionaries containing default colors, lines, and 
+        markers to use for plotting for the number of pairs in analysis class
+        
+    """
+    x = [dict(color='b', linestyle='--',marker='x'),
+         dict(color='g', linestyle='-.',marker='o'),
+         dict(color='r', linestyle=':',marker='v'),
+         dict(color='c', linestyle='--',marker='^'),
+         dict(color='m', linestyle='-.',marker='s')]
+    #Repeat these 5 instances over and over if more than 5 lines.
+    return x[p_index % 5]
+
+def new_color_map():
+    """Creates new color map for difference plots
+    
+    Returns
+    -------
+    colormap
+        Orange and blue color map
+        
+    """
+    top = mpl.cm.get_cmap('Blues_r', 128)
+    bottom = mpl.cm.get_cmap('Oranges', 128)
+    newcolors = np.vstack((top(np.linspace(0, 1, 128)),
+                           bottom(np.linspace(0, 1, 128))))
+    return ListedColormap(newcolors, name='OrangeBlue')
+
+def map_projection(f):
+    """Defines map projection. This needs updating to make it more generic.
+    
+    Parameters
+    ----------
+    f : class
+        model class
+        
+    Returns
+    -------
+    cartopy projection 
+        projection to be used by cartopy in plotting
+        
+    """
+    import cartopy.crs as ccrs
+    if f.model.lower() == 'cmaq':
+        proj = ccrs.LambertConformal(
+            central_longitude=f.obj.XCENT, central_latitude=f.obj.YCENT)
+    elif f.model.lower() == 'wrfchem' or f.model.lower() == 'rapchem':
+        if f.obj.MAP_PROJ == 1:
+            proj = ccrs.LambertConformal(
+                central_longitude=f.obj.CEN_LON, central_latitude=f.obj.CEN_LAT)
+        elif f.MAP_PROJ == 6:
+            #Plate Carree is the equirectangular or equidistant cylindrical
+            proj = ccrs.PlateCarree(
+                central_longitude=f.obj.CEN_LON)
+        else:
+            raise NotImplementedError('WRFChem projection not supported. Please add to surfplots.py')         
+    #Need to add the projections you want to use for the other models here.        
+    elif f.model.lower() == 'rrfs':
+        proj = ccrs.LambertConformal(
+            central_longitude=f.obj.cen_lon, central_latitude=f.obj.cen_lat)
+    elif f.model.lower() in ['cesm_fv','cesm_se','raqms']:
+        proj = ccrs.PlateCarree()
+    elif f.model.lower() == 'random':
+        proj = ccrs.PlateCarree()
+    else: #Let's change this tomorrow to just plot as lambert conformal if nothing provided.
+        raise NotImplementedError('Projection not defined for new model. Please add to surfplots.py')
+    return proj
+    
+def make_timeseries(df, df_reg=None,column=None, label=None, ax=None, avg_window=None, ylabel=None,
+                    vmin = None, vmax = None,
+                    domain_type=None, domain_name=None,
+                    plot_dict=None, fig_dict=None, text_dict=None,debug=False):
+    """Creates timeseries plot. 
+    
+    Parameters
+    ----------
+    df : dataframe
+        model/obs pair data to plot
+    column : str
+        Column label of variable to plot
+    df_reg: not currently enabled. empty argument for symmetry with surfplots
+        model/obs paired regulatory data to plot
+    label : str
+        Name of variable to use in plot legend 
+    ax : ax
+        matplotlib ax from previous occurrence so can overlay obs and model 
+        results on the same plot
+    avg_window : rule 
+        Pandas resampling rule (e.g., 'H', 'D')
+    ylabel : str
+        Title of y-axis
+    vmin : real number
+        Min value to use on y-axis
+    vmax : real number
+        Max value to use on y-axis
+    domain_type : str
+        Domain type specified in input yaml file
+    domain_name : str
+        Domain name specified in input yaml file
+    plot_dict : dictionary
+        Dictionary containing information about plotting for each pair 
+        (e.g., color, linestyle, markerstyle)   
+    fig_dict : dictionary
+        Dictionary containing information about figure
+    text_dict : dictionary
+        Dictionary containing information about text
+    debug : boolean
+        Whether to plot interactively (True) or not (False). Flag for 
+        submitting jobs to supercomputer turn off interactive mode.
+        
+    Returns
+    -------
+    ax 
+        matplotlib ax such that driver.py can iterate to overlay multiple models on the 
+        same plot
+        
+    """
+    if debug == False:
+        plt.ioff()
+    #First define items for all plots
+    #set default text size
+    def_text = dict(fontsize=14)
+    if text_dict is not None:
+        text_kwargs = {**def_text, **text_dict}
+    else:
+        text_kwargs = def_text
+    # set ylabel to column if not specified.
+    if ylabel is None:
+        ylabel = column
+    if label is not None:
+        plot_dict['label'] = label
+    if vmin is not None and vmax is not None:
+        plot_dict['ylim'] = [vmin,vmax]
+    #scale the fontsize for the x and y labels by the text_kwargs
+    plot_dict['fontsize'] = text_kwargs['fontsize']*0.8
+    
+    #Then, if no plot has been created yet, create a plot and plot the obs.
+    if ax is None: 
+        #First define the colors for the observations.
+        obs_dict = dict(color='k', linestyle='-',marker='*', linewidth=1.2, markersize=6.)
+        if plot_dict is not None:
+            #Whatever is not defined in the yaml file is filled in with the obs_dict here.
+            plot_kwargs = {**obs_dict, **plot_dict}
+        else:
+            plot_kwargs = obs_dict
+        # create the figure
+        if fig_dict is not None:
+            f,ax = plt.subplots(**fig_dict)    
+        else: 
+            f,ax = plt.subplots(figsize=(10,6))
+        # plot the line
+        print(plot_kwargs)
+        # {'color': 'k', 'linestyle': '-', 'marker': '*', 'linewidth': 2.0, 'markersize': 10.0, 'label': 'omps_nm', 'fontsize': 14.4}
+        if avg_window is None:
+            df[column].mean('y').plot(ax=ax, color=plot_kwargs['color'],linestyle=plot_kwargs['linestyle'],\
+                                           marker=plot_kwargs['marker'],linewidth=plot_kwargs['linewidth'],\
+                                          markersize=plot_kwargs['markersize'],label=plot_kwargs['label'])
+        else:
+            df[column].resample(time = avg_window).mean().mean('y').plot(ax=ax,color=plot_kwargs['color'],\
+                                                                              linestyle=plot_kwargs['linestyle'],\
+                                           marker=plot_kwargs['marker'],linewidth=plot_kwargs['linewidth'],\
+                                          markersize=plot_kwargs['markersize'],label=plot_kwargs['label'])
+    
+    # If plot has been created add to the current axes.
+    else:
+        # this means that an axis handle already exists and use it to plot the model output.
+        if avg_window is None:
+            df[column].mean('y').plot(ax=ax, color=plot_dict['color'],linestyle=plot_dict['linestyle'],\
+                                           marker=plot_dict['marker'],linewidth=plot_dict['linewidth'],\
+                                          markersize=plot_dict['markersize'],label=plot_dict['label'])
+        else:
+            df[column].resample(time=avg_window).mean().mean('y').plot(ax=ax, color=plot_dict['color'],\
+                                                                            linestyle=plot_dict['linestyle'],\
+                                           marker=plot_dict['marker'],linewidth=plot_dict['linewidth'],\
+                                          markersize=plot_dict['markersize'],label=plot_dict['label'])   
+    
+    #Set parameters for all plots
+    ax.set_ylabel(ylabel,fontweight='bold',**text_kwargs)
+    ax.set_xlabel('time',fontweight='bold',**text_kwargs)
+    ax.legend(frameon=False,fontsize=text_kwargs['fontsize']*0.8)
+    ax.tick_params(axis='both',length=10.0,direction='inout')
+    ax.tick_params(axis='both',which='minor',length=5.0,direction='out')
+    ax.legend(frameon=False,fontsize=text_kwargs['fontsize']*0.8,
+              bbox_to_anchor=(1.0, 0.9), loc='center left')
+    if domain_type is not None and domain_name is not None:
+        if domain_type == 'epa_region':
+            ax.set_title('EPA Region ' + domain_name,fontweight='bold',**text_kwargs)
+        else:
+            ax.set_title(domain_name,fontweight='bold',**text_kwargs)
+    return ax
+    
+def make_taylor(df,df_reg=None, column_o=None, label_o='Obs', column_m=None, label_m='Model', 
+                dia=None, ylabel=None, ty_scale=1.5,
+                domain_type=None, domain_name=None,
+                plot_dict=None, fig_dict=None, text_dict=None,debug=False):
+    """Creates taylor plot. Note sometimes model values are off the scale 
+    on this plot. This will be fixed soon.
+    
+    Parameters
+    ----------
+    df : dataframe
+        model/obs pair data to plot
+    df_reg: not currently enabled. empty argument for symmetry with surfplots
+        model/obs paired regulatory data to plot
+    column_o : str
+        Column label of observational variable to plot
+    label_o : str
+        Name of observational variable to use in plot legend
+    column_m : str
+        Column label of model variable to plot
+    label_m : str
+        Name of model variable to use in plot legend 
+    dia : dia
+        matplotlib ax from previous occurrence so can overlay obs and model 
+        results on the same plot
+    ylabel : str
+        Title of x-axis
+    ty_scale : real
+        Scale to apply to taylor plot to control the plotting range
+    domain_type : str
+        Domain type specified in input yaml file
+    domain_name : str
+        Domain name specified in input yaml file
+    plot_dict : dictionary
+        Dictionary containing information about plotting for each pair 
+        (e.g., color, linestyle, markerstyle)   
+    fig_dict : dictionary
+        Dictionary containing information about figure
+    text_dict : dictionary
+        Dictionary containing information about text
+    debug : boolean
+        Whether to plot interactively (True) or not (False). Flag for 
+        submitting jobs to supercomputer turn off interactive mode.
+        
+    Returns
+    -------
+    class 
+        Taylor diagram class defined in MONET
+        
+    """
+    nan_ind = ((~np.isnan(df[column_o].values))&(~np.isnan(df[column_m].values)))
+    #First define items for all plots
+    if debug == False:
+        plt.ioff()
+        
+    #set default text size
+    def_text = dict(fontsize=14.0)
+    if text_dict is not None:
+        text_kwargs = {**def_text, **text_dict}
+    else:
+        text_kwargs = def_text
+    # set ylabel to column if not specified.
+    if ylabel is None:
+        ylabel = column_o
+    #Then, if no plot has been created yet, create a plot and plot the first pair.
+    if dia is None: 
+        # create the figure
+        if fig_dict is not None:
+            f = plt.figure(**fig_dict)    
+        else: 
+            f = plt.figure(figsize=(12,10))    
+        sns.set_style('ticks')
+        # plot the line
+        dia = td(df[column_o].std().values, scale=ty_scale, fig=f,
+                               rect=111, label=label_o)
+        plt.grid(linewidth=1, alpha=.5)
+        cc = corrcoef(df[column_o].values[nan_ind].flatten(), df[column_m].values[nan_ind].flatten())[0, 1]
+        dia.add_sample(df[column_m].std().values, cc, zorder=9, label=label_m, **plot_dict)
+    # If plot has been created add to the current axes.
+    else:
+        # this means that an axis handle already exists and use it to plot another model
+        cc = corrcoef(df[column_o].values[nan_ind].flatten(), df[column_m].values[nan_ind].flatten())[0, 1]
+        dia.add_sample(df[column_m].std().values, cc, zorder=9, label=label_m, **plot_dict)
+    #Set parameters for all plots
+    contours = dia.add_contours(colors='0.5')
+    plt.clabel(contours, inline=1, fontsize=text_kwargs['fontsize']*0.8)
+    plt.grid(alpha=.5)
+    plt.legend(frameon=False,fontsize=text_kwargs['fontsize']*0.8,
+               bbox_to_anchor=(0.75, 0.93), loc='center left')
+    if domain_type is not None and domain_name is not None:
+        if domain_type == 'epa_region':
+            plt.title('EPA Region ' + domain_name,fontweight='bold',**text_kwargs)
+        else:
+            plt.title(domain_name,fontweight='bold',**text_kwargs)
+    ax = plt.gca()
+    ax.axis["left"].label.set_text('Standard Deviation: '+ylabel)
+    ax.axis["top"].label.set_text('Correlation')
+    ax.axis["left"].label.set_fontsize(text_kwargs['fontsize'])
+    ax.axis["top"].label.set_fontsize(text_kwargs['fontsize'])
+    ax.axis["left"].label.set_fontweight('bold')
+    ax.axis["top"].label.set_fontweight('bold')
+    ax.axis["top"].major_ticklabels.set_fontsize(text_kwargs['fontsize']*0.8)
+    ax.axis["left"].major_ticklabels.set_fontsize(text_kwargs['fontsize']*0.8)
+    ax.axis["right"].major_ticklabels.set_fontsize(text_kwargs['fontsize']*0.8)
+    return dia
+
+def make_spatial_overlay(df, vmodel, column_o=None, label_o=None, column_m=None, 
+                      label_m=None, ylabel = None, vmin=None,
+                      vmax = None, nlevels = None, proj = None, outname = 'plot', 
+                      domain_type=None, domain_name=None, fig_dict=None, 
+                      text_dict=None,debug=False):
+        
+    """Creates spatial overlay plot. 
+    
+    Parameters
+    ----------
+    df : dataframe
+        model/obs pair data to plot
+    vmodel: dataarray
+        slice of model data to plot
+    column_o : str
+        Column label of observation variable to plot
+    label_o : str
+        Name of observation variable to use in plot title 
+    column_m : str
+        Column label of model variable to plot
+    label_m : str
+        Name of model variable to use in plot title
+    ylabel : str
+        Title of colorbar axis
+    vmin : real number
+        Min value to use on colorbar axis
+    vmax : real number
+        Max value to use on colorbar axis
+    nlevels: integer
+        Number of levels used in colorbar axis
+    proj: cartopy projection
+        cartopy projection to use in plot
+    outname : str
+        file location and name of plot (do not include .png)
+    domain_type : str
+        Domain type specified in input yaml file
+    domain_name : str
+        Domain name specified in input yaml file
+    fig_dict : dictionary
+        Dictionary containing information about figure
+    text_dict : dictionary
+        Dictionary containing information about text
+    debug : boolean
+        Whether to plot interactively (True) or not (False). Flag for 
+        submitting jobs to supercomputer turn off interactive mode.
+        
+    Returns
+    -------
+    plot 
+        spatial overlay plot
+        
+    """
+    if debug == False:
+        plt.ioff()
+        
+    def_map = dict(states=True,figsize=[15, 8])
+    if fig_dict is not None:
+        map_kwargs = {**def_map, **fig_dict}
+    else:
+        map_kwargs = def_map
+  
+    #set default text size
+    def_text = dict(fontsize=20)
+    if text_dict is not None:
+        text_kwargs = {**def_text, **text_dict}
+    else:
+        text_kwargs = def_text
+        
+    # set ylabel to column if not specified.
+    if ylabel is None:
+        ylabel = column_o
+    
+    #Take the mean for each siteid
+    df_mean=df.groupby(['siteid'],as_index=False).mean()
+    
+    #Take the mean over time for the model output
+    vmodel_mean = vmodel[column_m].mean(dim='time').squeeze()
+    
+    #Determine the domain
+    if domain_type == 'all' and domain_name == 'CONUS':
+        latmin= 25.0
+        lonmin=-130.0
+        latmax= 50.0
+        lonmax=-60.0
+        title_add = domain_name + ': '
+    elif domain_type == 'epa_region' and domain_name is not None:
+        latmin,lonmin,latmax,lonmax,acro = get_epa_bounds(index=None,acronym=domain_name)
+        title_add = 'EPA Region ' + domain_name + ': '
+    else:
+        latmin= math.floor(min(df.latitude))
+        lonmin= math.floor(min(df.longitude))
+        latmax= math.ceil(max(df.latitude))
+        lonmax= math.ceil(max(df.longitude))
+        title_add = domain_name + ': '
+    
+    #Map the model output first.
+    cbar_kwargs = dict(aspect=15,shrink=.8)
+    
+    #Add options that this could be included in the fig_kwargs in yaml file too.
+    if 'extent' not in map_kwargs:
+        map_kwargs['extent'] = [lonmin,lonmax,latmin,latmax] 
+    if 'crs' not in map_kwargs:
+        map_kwargs['crs'] = proj
+    
+    #With pcolormesh, a Warning shows because nearest interpolation may not work for non-monotonically increasing regions.
+    #Because I do not want to pull in the edges of the lat lon for every model I switch to contourf.
+    #First determine colorbar, so can use the same for both contourf and scatter
+    if vmin == None and vmax == None:
+        vmin = np.min((vmodel_mean.quantile(0.01), df_mean[column_o].quantile(0.01)))
+        vmax = np.max((vmodel_mean.quantile(0.99), df_mean[column_o].quantile(0.99)))
+        
+    if nlevels == None:
+        nlevels = 21
+    
+    clevel = np.linspace(vmin,vmax,nlevels)
+    cmap = mpl.cm.get_cmap('Spectral_r',nlevels-1) 
+    norm = mpl.colors.BoundaryNorm(clevel, ncolors=cmap.N, clip=False)
+        
+    #I add extend='both' here because the colorbar is setup to plot the values outside the range
+    ax = vmodel_mean.monet.quick_contourf(cbar_kwargs=cbar_kwargs, figsize=map_kwargs['figsize'], map_kws=map_kwargs,
+                                robust=True, norm=norm, cmap=cmap, levels=clevel, extend='both') 
+    
+    plt.gcf().canvas.draw() 
+    plt.tight_layout(pad=0)
+    plt.title(title_add + label_o + ' overlaid on ' + label_m,fontweight='bold',**text_kwargs)
+     
+    ax.axes.scatter(df_mean.longitude.values, df_mean.latitude.values,s=30,c=df_mean[column_o], 
+                    transform=ccrs.PlateCarree(), edgecolor='b', linewidth=.50, norm=norm, 
+                    cmap=cmap)
+    ax.axes.set_extent(map_kwargs['extent'],crs=ccrs.PlateCarree())    
+    
+    #Uncomment these lines if you update above just to verify colorbars are identical.
+    #Also specify plot above scatter = ax.axes.scatter etc.
+    #cbar = ax.figure.get_axes()[1] 
+    #plt.colorbar(scatter,ax=ax)
+    
+    #Update colorbar
+    f = plt.gcf()
+    model_ax = f.get_axes()[0]
+    cax = f.get_axes()[1]
+    #get the position of the plot axis and use this to rescale nicely the color bar to the height of the plot.
+    position_m = model_ax.get_position()
+    position_c = cax.get_position()
+    cax.set_position([position_c.x0, position_m.y0, position_c.x1 - position_c.x0, (position_m.y1-position_m.y0)*1.1])
+    cax.set_ylabel(ylabel,fontweight='bold',**text_kwargs)
+    cax.tick_params(labelsize=text_kwargs['fontsize']*0.8,length=10.0,width=2.0,grid_linewidth=2.0)    
+    
+    #plt.tight_layout(pad=0)
+    savefig(outname + '.png',loc=4, height=100, decorate=True, bbox_inches='tight', dpi=150)
+    return ax
+    
+def calculate_boxplot(df, df_reg=None,column=None, label=None, plot_dict=None, comb_bx = None, label_bx = None):
+    """Combines data into acceptable format for box-plot
+    
+    Parameters
+    ----------
+    df : dataframe
+         model/obs pair data to plot
+    df_reg: not currently enabled. empty argument for symmetry with surfplots
+        model/obs paired regulatory data to plot
+    column : str
+        Column label of variable to plot
+    label : str
+        Name of variable to use in plot legend
+    comb_bx: dataframe
+        dataframe containing information to create box-plot from previous 
+        occurrence so can overlay multiple model results on plot
+    label_bx: list
+        list of string labels to use in box-plot from previous occurrence so 
+        can overlay multiple model results on plot
+    Returns
+    -------
+    dataframe, list
+        dataframe containing information to create box-plot
+        list of string labels to use in box-plot
+        
+    """
+    if comb_bx is None and label_bx is None:
+        comb_bx = pd.DataFrame()
+        label_bx = []
+        #First define the colors for the observations.
+        obs_dict = dict(color='gray', linestyle='-',marker='x', linewidth=1.2, markersize=6.)
+        if plot_dict is not None:
+            #Whatever is not defined in the yaml file is filled in with the obs_dict here.
+            plot_kwargs = {**obs_dict, **plot_dict}
+        else:
+            plot_kwargs = obs_dict
+    else:
+        plot_kwargs = plot_dict
+    #For all, a column to the dataframe and append the label info to the list.
+    plot_kwargs['column'] = column
+    plot_kwargs['label'] = label
+    comb_bx[label] = df[column]
+    label_bx.append(plot_kwargs)
+    
+    return comb_bx, label_bx
+    
+def make_boxplot(comb_bx, label_bx, ylabel = None, vmin = None, vmax = None, outname='plot',
+                 domain_type=None, domain_name=None,
+                 plot_dict=None, fig_dict=None,text_dict=None,debug=False):
+    
+    """Creates box-plot. 
+    
+    Parameters
+    ----------
+    comb_bx: dataframe
+        dataframe containing information to create box-plot from 
+        calculate_boxplot
+    label_bx: list
+        list of string labels to use in box-plot from calculate_boxplot
+    ylabel : str
+        Title of y-axis
+    vmin : real number
+        Min value to use on y-axis
+    vmax : real number
+        Max value to use on y-axis
+    outname : str
+        file location and name of plot (do not include .png)
+    domain_type : str
+        Domain type specified in input yaml file
+    domain_name : str
+        Domain name specified in input yaml file
+    plot_dict : dictionary
+        Dictionary containing information about plotting for each pair 
+        (e.g., color, linestyle, markerstyle)   
+    fig_dict : dictionary
+        Dictionary containing information about figure
+    text_dict : dictionary
+        Dictionary containing information about text
+    debug : boolean
+        Whether to plot interactively (True) or not (False). Flag for 
+        submitting jobs to supercomputer turn off interactive mode.
+        
+    Returns
+    -------
+    plot 
+        box plot
+        
+    """
+    if debug == False:
+        plt.ioff()
+    #First define items for all plots
+    #set default text size
+    def_text = dict(fontsize=14)
+    if text_dict is not None:
+        text_kwargs = {**def_text, **text_dict}
+    else:
+        text_kwargs = def_text
+    # set ylabel to column if not specified.
+    if ylabel is None:
+        ylabel = label_bx[0]
+    
+    #Fix the order and palate colors
+    order_box = []
+    pal = {}
+    for i in range(len(label_bx)):
+        order_box.append(label_bx[i]['label'])
+        pal[label_bx[i]['label']] = label_bx[i]['color']
+        
+    #Make plot
+    if fig_dict is not None:
+        f,ax = plt.subplots(**fig_dict)    
+    else: 
+        f,ax = plt.subplots(figsize=(8,8))
+    #Define characteristics of boxplot.
+    boxprops = {'edgecolor': 'k', 'linewidth': 1.5}
+    lineprops = {'color': 'k', 'linewidth': 1.5}
+    boxplot_kwargs = {'boxprops': boxprops, 'medianprops': lineprops,
+                  'whiskerprops': lineprops, 'capprops': lineprops,
+                  'fliersize' : 2.0, 
+                  'flierprops': dict(marker='*', 
+                                     markerfacecolor='blue', 
+                                     markeredgecolor='none',
+                                     markersize = 6.0),
+                  'width': 0.75, 'palette': pal,
+                  'order': order_box,
+                  'showmeans': True, 
+                  'meanprops': {'marker': ".", 'markerfacecolor': 'black', 
+                                'markeredgecolor': 'black',
+                               'markersize': 20.0}}
+    sns.set_style("whitegrid")
+    sns.set_style("ticks")
+    sns.boxplot(ax=ax,x="variable", y="value",data=pd.melt(comb_bx), **boxplot_kwargs)
+    ax.set_xlabel('')
+    ax.set_ylabel(ylabel,fontweight='bold',**text_kwargs)
+    ax.tick_params(labelsize=text_kwargs['fontsize']*0.8)
+    if domain_type is not None and domain_name is not None:
+        if domain_type == 'epa_region':
+            ax.set_title('EPA Region ' + domain_name,fontweight='bold',**text_kwargs)
+        else:
+            ax.set_title(domain_name,fontweight='bold',**text_kwargs)
+    if vmin is not None and vmax is not None:
+        ax.set_ylim(ymin = vmin, ymax = vmax)
+    
+    plt.tight_layout()
+    savefig(outname + '.png',loc=4, height=100, decorate=True, bbox_inches='tight', dpi=200)
+    
+def make_spatial_bias_gridded(df, column_o=None, label_o=None, column_m=None, 
+                      label_m=None, ylabel = None, vmin=None,
+                      vmax = None, nlevels = None, proj = None, outname = 'plot', 
+                      domain_type=None, domain_name=None, fig_dict=None, 
+                      text_dict=None,debug=False):
+        
+    """Creates difference plot for satellite and model data. Needs to be altered for cases where more than 1 overpass for a location,
+    eg. more than 1 day of data."""
+    if debug == False:
+        plt.ioff()
+        
+    def_map = dict(states=True,figsize=[15, 8])
+    if fig_dict is not None:
+        map_kwargs = {**def_map, **fig_dict}
+    else:
+        map_kwargs = def_map
+  
+    #set default text size
+    def_text = dict(fontsize=20)
+    if text_dict is not None:
+        text_kwargs = {**def_text, **text_dict}
+    else:
+        text_kwargs = def_text
+        
+    # set ylabel to column if not specified.
+    if ylabel is None:
+        ylabel = column_o
+    
+    #Take the difference for the model output - the sat output
+    diff_mod_min_obs = (df[column_o] - df[column_m]).squeeze()
+
+    
+    #Determine the domain
+    if domain_type == 'all' and domain_name == 'CONUS':
+        latmin= 25.0
+        lonmin=-130.0
+        latmax= 50.0
+        lonmax=-60.0
+        title_add = domain_name + ': '
+    elif domain_type == 'epa_region' and domain_name is not None:
+        latmin,lonmin,latmax,lonmax,acro = get_epa_bounds(index=None,acronym=domain_name)
+        title_add = 'EPA Region ' + domain_name + ': '
+    else:
+        latmin= -90
+        lonmin= -180
+        latmax= 90
+        lonmax= 180
+        title_add = domain_name + ': '
+    
+    #Map the model output first.
+    cbar_kwargs = dict(aspect=15,shrink=.8)
+    
+    #Add options that this could be included in the fig_kwargs in yaml file too.
+    if 'extent' not in map_kwargs:
+        map_kwargs['extent'] = [lonmin,lonmax,latmin,latmax] 
+    if 'crs' not in map_kwargs:
+        map_kwargs['crs'] = proj
+    
+    #First determine colorbar
+    if vmin == None and vmax == None:
+        #vmin = vmodel_mean.quantile(0.01)
+        vmax = np.max((np.abs(diff_mod_min_obs.quantile(0.99)),np.abs(diff_mod_min_obs.quantile(0.01))))
+        vmin = -vmax
+        
+    if nlevels == None:
+        nlevels = 21
+    print(vmin,vmax)
+    clevel = np.linspace(vmin,vmax,nlevels)
+    cmap = mpl.cm.get_cmap('bwr',nlevels-1) 
+    norm = mpl.colors.BoundaryNorm(clevel, ncolors=cmap.N, clip=False)
+        
+    #I add extend='both' here because the colorbar is setup to plot the values outside the range
+    ax = monet.plots.mapgen.draw_map(crs=map_kwargs['crs'],extent=map_kwargs['extent'])
+    # draw scatter plot of model and satellite differences
+    c = ax.axes.scatter(df.longitude,df.latitude,c=diff_mod_min_obs,cmap=cmap,s=2,norm=norm)
+    plt.gcf().canvas.draw() 
+    plt.tight_layout(pad=0)
+    plt.title(title_add + label_o + ' - ' + label_m,fontweight='bold',**text_kwargs)
+    ax.axes.set_extent(map_kwargs['extent'],crs=ccrs.PlateCarree())    
+    
+    #Uncomment these lines if you update above just to verify colorbars are identical.
+    #Also specify plot above scatter = ax.axes.scatter etc.
+    #cbar = ax.figure.get_axes()[1] 
+    plt.colorbar(c,ax=ax,extend='both')
+    
+    #Update colorbar
+    f = plt.gcf()
+    
+    model_ax = f.get_axes()[0]
+    cax = f.get_axes()[1]
+    
+    #get the position of the plot axis and use this to rescale nicely the color bar to the height of the plot.
+    position_m = model_ax.get_position()
+    position_c = cax.get_position()
+    cax.set_position([position_c.x0, position_m.y0, position_c.x1 - position_c.x0, (position_m.y1-position_m.y0)*1.1])
+    cax.set_ylabel(ylabel,fontweight='bold',**text_kwargs)
+    cax.tick_params(labelsize=text_kwargs['fontsize']*0.8,length=10.0,width=2.0,grid_linewidth=2.0)    
+    
+    #plt.tight_layout(pad=0)
+    savefig(outname + '.png',loc=4, height=100, decorate=True, bbox_inches='tight', dpi=150)
+    return ax    
diff --git a/melodies_monet/tests/test_analysis_util.py b/melodies_monet/tests/test_analysis_util.py
new file mode 100644
index 00000000..43d61951
--- /dev/null
+++ b/melodies_monet/tests/test_analysis_util.py
@@ -0,0 +1,36 @@
+# Copyright (C) 2022 National Center for Atmospheric Research and National Oceanic and Atmospheric Administration
+# SPDX-License-Identifier: Apache-2.0
+#
+import os
+import pytest
+from datetime import datetime
+
+from melodies_monet.util import analysis_util
+
+
+def test_fill_date_template():
+
+    date = datetime.now()
+    date_str = date.strftime('%Y-%m-%b-%d-%j')
+    print(date_str)
+
+    template_str = 'Year YYYY, Month MM, Month Name M_ABBR, Day DD'
+    filled_str = analysis_util.fill_date_template(template_str, date_str)
+    print(filled_str)
+    assert(filled_str == date.strftime('Year %Y, Month %m, Month Name %b, Day %d'))
+
+    template_str = 'Year YYYY, Julian Day DDD'
+    filled_str = analysis_util.fill_date_template(template_str, date_str)
+    print(filled_str)
+    assert(filled_str == date.strftime('Year %Y, Julian Day %j'))
+
+
+def test_find_file(tmpdir):
+
+    test_file = os.path.join(tmpdir, 'test.txt')
+    f = open(test_file, 'w')
+    f.close()
+
+    filename = analysis_util.find_file(tmpdir, 'test*')
+    print(filename)
+    assert(filename == test_file)
diff --git a/melodies_monet/tests/test_get_data_cli.py b/melodies_monet/tests/test_get_data_cli.py
index 73e794e6..515e7e9c 100644
--- a/melodies_monet/tests/test_get_data_cli.py
+++ b/melodies_monet/tests/test_get_data_cli.py
@@ -29,11 +29,11 @@ def test_get_aeronet(tmp_path):
     # since positions may differ due to NaN-lat/lon dropping or such
     ds = xr.open_dataset(tmp_path / fn).squeeze().swap_dims(x="siteid")
     ds0 = ds0_aeronet.sel(time=ds.time).squeeze().swap_dims(x="siteid")
-    # TODO: seems original loading missing value as -1 (on purpose, due to compress routine)
+    # NOTE: -1 in ds0 indicates missing value, due to compress routine
     
     assert not ds.identical(ds0)
     assert ds.time.equals(ds0.time)
-    # assert (np.abs(ds.aod_551nm - ds0.aod_551nm) < 1e-9).all()
+    ds0["aod_551nm"] = ds0["aod_551nm"].where(ds0["aod_551nm"] != -1)
     assert (np.abs(ds.aod_551nm - ds0.aod_551nm).to_series().dropna() < 1e-9).all()
     # - Many more site IDs in ds0 (400 vs 283), and one that is in ds but not ds0
     # - In the above, only two sites
diff --git a/melodies_monet/util/__init__.py b/melodies_monet/util/__init__.py
index 1cb8d99d..3dda947e 100644
--- a/melodies_monet/util/__init__.py
+++ b/melodies_monet/util/__init__.py
@@ -1,4 +1,4 @@
 # Copyright (C) 2022 National Center for Atmospheric Research and National Oceanic and Atmospheric Administration
 # SPDX-License-Identifier: Apache-2.0
 #
-__all__ = ['write_util','read_util', 'tools']
+__all__ = ['write_util','read_util','tools','satellite_utilites','hdfio','test_hdfio']
diff --git a/melodies_monet/util/analysis_util.py b/melodies_monet/util/analysis_util.py
new file mode 100644
index 00000000..7c888ae2
--- /dev/null
+++ b/melodies_monet/util/analysis_util.py
@@ -0,0 +1,87 @@
+# Copyright (C) 2022 National Center for Atmospheric Research and National Oceanic and Atmospheric Administration
+# SPDX-License-Identifier: Apache-2.0
+#
+
+import os
+import logging
+from glob import glob
+
+
+def fill_date_template(template_str, date_str):
+    """
+    Replace date template parameters with values from date string
+
+    Parameters
+        template_str (str): template string
+        date_str (str yyyy-mm-m_abbr-dd-ddd): date string
+
+    Returns
+        template_str (str): filled template string
+    """
+
+    yyyy_str, mm_str, m_abbr_str, dd_str, ddd_str \
+        = tuple(date_str.split('-'))
+
+    if 'DDD' in template_str:
+        return template_str.replace(
+            'YYYY', yyyy_str).replace(
+            'DDD', ddd_str)
+    else:
+        return template_str.replace(
+            'YYYY', yyyy_str).replace(
+            'MM', mm_str).replace(
+            'M_ABBR', m_abbr_str).replace(
+            'DD', dd_str)
+
+
+def find_file(datadir, filestr):
+    """
+    Parameters
+        datadir (str): data directory
+        filestr (str): filename regular expression
+
+    Returns
+        filename (str): complete path of matching filename in data directory
+    """
+    logger = logging.getLogger(__name__)
+
+    pattern = os.path.join(os.path.expandvars(datadir), filestr)
+    files = glob(pattern)
+
+    if len(files) == 0:
+        raise Exception('no file matches for %s' % pattern)
+    if len(files) > 1:
+        raise Exception('more than one file match %s' % pattern)
+
+    filename = files[0]
+    logger.info(filename)
+
+    return filename
+
+
+def get_obs_vars(config):
+    """
+    Get subset of obs variables from model to obs variable mapping
+
+    Parameters
+        config (dict): configuration dictionary
+
+    Returns
+        obs_vars_subset (dict of dict):
+            nested dictionary keyed by obs set name and obs variable name
+    """
+    obs_vars_subset = dict()
+
+    for model_name in config['model']:
+
+        mapping = config['model'][model_name]['mapping']
+
+        for obs_name in mapping:
+            obs_vars = config['obs'][obs_name]['variables']
+            obs_vars_subset[obs_name] = dict()
+
+            for model_var in mapping[obs_name]:
+                obs_var = mapping[obs_name][model_var]
+                obs_vars_subset[obs_name][obs_var] = obs_vars[obs_var]
+
+    return obs_vars_subset
diff --git a/melodies_monet/util/read_grid_util.py b/melodies_monet/util/read_grid_util.py
new file mode 100644
index 00000000..d54c7b8c
--- /dev/null
+++ b/melodies_monet/util/read_grid_util.py
@@ -0,0 +1,97 @@
+# Copyright (C) 2022 National Center for Atmospheric Research and National Oceanic and Atmospheric Administration
+# SPDX-License-Identifier: Apache-2.0
+#
+import os
+import logging
+import xarray as xr
+from monetio.sat._gridded_eos_mm import read_gridded_eos
+
+from .analysis_util import fill_date_template, find_file
+
+
+def read_grid_models(config, date_str, model=None):
+    """
+    Read grid data models
+
+    Parameters
+        config (dict): configuration dictionary
+        date_str (str yyyy-mm-m_abbr-dd-ddd): date string
+        model: specific model to read optional, if not specified all models in config['models'] will be read
+
+    Returns
+        model_datasets (dict of xr.Dataset): dictionary of model datasets
+        filenames (dict of str): dictionary of filenames
+    """
+    model_datasets = dict()
+    filenames = dict()
+
+    if model is not None:
+        model_list = [model]
+    else:
+        model_list = config['model']
+
+    for model_name in model_list:
+
+        datadir = config['model'][model_name]['datadir']
+        filestr = fill_date_template(
+            config['model'][model_name]['files'], date_str)
+        filename = find_file(datadir, filestr)
+
+        model_datasets[model_name] = xr.open_dataset(filename)
+        filenames[model_name] = filename
+
+    return model_datasets, filenames
+
+
+def read_grid_obs(config, obs_vars, date_str, obs=None):
+    """
+    Read grid data obs
+
+    Parameters
+        config (dict): configuration dictionary
+        obs_vars (dict of dict): nested dictionary keyed by obs set name and obs variable name
+        date_str (str yyyy-mm-m_abbr-dd-ddd): date string
+        obs: specific observation to read, optional, if not specified all obs in obs_vars will be read
+
+    Returns
+        obs_datasets (dict of xr.Dataset): dictionary of obs datasets
+        filenames (dict of str): dictionary of filenames
+    """
+    obs_datasets = dict()
+    filenames = dict()
+
+    if obs is not None:
+        obs_list = [obs]
+    else:
+        obs_list = obs_vars.keys()
+
+    yyyy_str, mm_str, m_abbr_str, dd_str, ddd_str \
+        = tuple(date_str.split('-'))
+
+    for obs_name in obs_list:
+
+        data_format = config['obs'][obs_name]['data_format']
+        datadir = config['obs'][obs_name]['datadir']
+        filestr = fill_date_template(
+            config['obs'][obs_name]['filename'], date_str)
+        filename = find_file(datadir, filestr)
+
+        file_extension = os.path.splitext(filename)[1]
+
+        if data_format == 'gridded_eos':
+            if file_extension == '.hdf':
+                ds_obs = read_gridded_eos(
+                    filename, obs_vars[obs_name])
+                filename_nc = filename.replace('.hdf', '.nc')
+                logging.info('writing ' + filename_nc)
+                ds_obs.to_netcdf(filename_nc)
+            else:
+                ds_obs = xr.open_dataset(filename)
+        else:
+            ds_obs = xr.open_dataset(filename)
+
+        obs_datasets[obs_name] = ds_obs
+        filenames[obs_name] = filename
+
+    return obs_datasets, filenames
+
diff --git a/melodies_monet/util/read_util.py b/melodies_monet/util/read_util.py
index 42c2bfd6..b505ebf2 100644
--- a/melodies_monet/util/read_util.py
+++ b/melodies_monet/util/read_util.py
@@ -127,16 +127,17 @@ def read_analysis_ncf(filenames,xr_kws={}):
     import xarray as xr
     
     if len(filenames)==1:
-        print('Reading: ', filenames[0])
+        print('Reading:', filenames[0])
         ds_out = xr.open_dataset(filenames[0],**xr_kws)
         
     elif len(filenames)>1:
         for count, file in enumerate(filenames):
-            print('Reading: ', file)
+            print('Reading:', file)
 
             if count==0:
                 ds_out = xr.open_dataset(file,**xr_kws)
                 group_name1 =  ds_out.attrs['group_name']
+
             else:
                 ds_append = xr.open_dataset(file,**xr_kws)
                 # Test if all the files have the same group to prevent merge issues
diff --git a/melodies_monet/util/regrid_util.py b/melodies_monet/util/regrid_util.py
new file mode 100644
index 00000000..1ccd2517
--- /dev/null
+++ b/melodies_monet/util/regrid_util.py
@@ -0,0 +1,57 @@
+# Copyright (C) 2022 National Center for Atmospheric Research and National Oceanic and Atmospheric Administration
+# SPDX-License-Identifier: Apache-2.0
+#
+
+"""
+file: regrid_util.py
+"""
+import os
+import xarray as xr
+
+
+def setup_regridder(config, config_group='obs'):
+    """
+    Setup regridder for observations or model
+
+    Parameters
+        config (dict): configuration dictionary
+
+    Returns
+        regridder (dict of xe.Regridder): dictionary of regridder instances
+    """
+    try:
+        import xesmf as xe
+    except ImportError as e:
+        print('regrid_util: xesmf module not found')
+        raise
+
+    target_file = os.path.expandvars(config['analysis']['target_grid'])
+    ds_target = xr.open_dataset(target_file)
+
+    regridder_dict = dict()
+
+    for name in config[config_group]:
+        base_file = os.path.expandvars(config[config_group][name]['regrid']['base_grid'])
+        ds_base = xr.open_dataset(base_file)
+        method = config[config_group][name]['regrid']['method']
+        regridder = xe.Regridder(ds_base, ds_target, method)
+        regridder_dict[name] = regridder
+
+    return regridder_dict
+
+
+def filename_regrid(filename, regridder):
+    """
+    Construct modified filename for regridded dataset
+
+    Parameters
+        filename (str): filename of dataset
+        regridder (xe.Regridder): regridder instance
+
+    Returns
+        filename_regrid (str): filename of regridded dataset
+    """
+    filename_regrid = filename.replace('.nc', '_regrid.nc')
+
+    return filename_regrid
+
diff --git a/melodies_monet/util/satellite_utilities.py b/melodies_monet/util/satellite_utilities.py
new file mode 100644
index 00000000..b47dcfa8
--- /dev/null
+++ b/melodies_monet/util/satellite_utilities.py
@@ -0,0 +1,236 @@
+# Copyright (C) 2022 National Center for Atmospheric Research and National Oceanic and Atmospheric Administration
+# SPDX-License-Identifier: Apache-2.0
+#
+# File started by Maggie Bruckner. 
+# Contains satellite specific pairing operators
+import numpy as np
+from datetime import datetime,timedelta
+
+def omps_l3_daily_o3_pairing(model_data,obs_data,ozone_ppbv_varname):
+    '''Calculate model ozone column from model ozone profile in ppbv. Move data from model grid 
+        to 1x1 degree OMPS L3 data grid. Following data grid matching, take daily mean for model data.
+    '''
+    try:
+        import xesmf as xe
+    except ImportError as e:
+        print('satellite_utilities: xesmf module not found')
+        raise
+    
+    # factor for converting ppbv profiles to DU column
+    # also requires conversion of dp from Pa to hPa
+    du_fac = 1.0e-5*6.023e23/28.97/9.8/2.687e19
+    column = (du_fac*(model_data['dp_pa']/100.)*model_data[ozone_ppbv_varname]).sum('z')
+    
+    # initialize regrid and apply to column data
+    grid_adjust = xe.Regridder(model_data[['latitude','longitude']],obs_data[['latitude','longitude']],'bilinear')
+    mod_col_obsgrid = grid_adjust(column)
+    # Aggregate time-step to daily means
+    daily_mean = mod_col_obsgrid.groupby('time.date').mean()
+    
+    # change dimension name for date to time
+    daily_mean = daily_mean.rename({'date':'time'})
+    return daily_mean
+
+def space_and_time_pairing(model_data,obs_data,pair_variables):
+    '''Bilinear spatial and temporal satellite pairing code. 
+    Assumes model data has (time,pressure,latitude,longitude) dimensions.
+    Assumes observation data contains fields named time, pressure, latiutde, and longitude.
+    
+    
+    *** need to make setup work for surface/1z fields, as some pairing requires surface pressure field *** 
+    '''
+    try:
+        import xesmf as xe
+    except ImportError as e:
+        print('satellite_utilities: xesmf module not found')
+        raise
+    mod_nf,mod_nz,mod_nx,mod_ny = model_data[pair_variables[0]].shape # assumes model data is structured (time,z,lon,lat). lon/lat dimension order likely unimportant
+    obs_nz = obs_data['pressure'].shape # assumes 1d pressure field in observation set
+    obs_nx,obs_ny = obs_data['longitude'].shape # assumes 2d lat/lon fields in observation ser
+    
+    # initialize dictionary and arrays for interpolated model data
+    ds = {i:np.zeros((mod_nz,obs_nx,obs_ny)) for i in pair_variables}
+    
+    # loop over model time steps
+    for f in range(mod_nf):
+        
+        # set index for observation data less than 1 model timestep from working model file.
+        tindex = np.where(np.abs(obs_data.time - model_data.time[f]) <= (model_data.time[1]-model_data.time[0]))[0]
+        
+        # if there is observation data within the selected time range, proceed with pairing
+        if len(tindex):
+            # initialize spatial regridder (model lat/lon to satellite swath lat/lon)
+            # dimensions of new variables will be (time, z, satellite_x, satellite_y)
+            regridr = xe.Regridder(model_data.isel(time=f),obs_data[['latitude','longitude']].sel(x=tindex),'bilinear') # standard bilinear spatial regrid. 
+            
+            # regrid for each variable in pair_variables
+            for j in pair_variables:
+                interm_var = regridr(model_data[j][f])
+                
+                # apply  time interpolation
+                if f == (mod_nf-1):
+                #    print('last')
+                    t2 = np.where((obs_data.time[tindex] >= model_data.time[f]))[0]
+                    ds[j][:,tindex[t2]] = interm_var[:,t2].values
+
+                    tind_2 = np.where((obs_data.time[tindex] < model_data.time[f]) & 
+                                      (np.abs(obs_data.time[tindex] - model_data.time[f]) <= (model_data.time[1]-model_data.time[0])))[0]
+                    tfac1 = 1-(np.abs(model_data.time[f] - obs_data.time[tindex[tind_2]])/(model_data.time[1]-model_data.time[0]))
+
+                    ds[j][:,tindex[tind_2]] += np.expand_dims(tfac1.values,axis=1)*interm_var[:,tind_2].values
+                
+                elif f == (0):
+                #    print('first')
+                    t2 = np.where((obs_data.time[tindex] <= model_data.time[f]))[0]
+                    ds[j][:,tindex[t2],:] = interm_var[:,t2].values
+                    
+                    tind_2 = np.where((obs_data.time[tindex] > model_data.time[f]) & 
+                                      (np.abs(obs_data.time[tindex] - model_data.time[f]) <= (model_data.time[1]-model_data.time[0])))[0]
+                    tfac1 = 1-(np.abs(model_data.time[f] - obs_data.time[tindex[tind_2]])/(model_data.time[1]-model_data.time[0]))
+
+                    ds[j][:,tindex[tind_2],:] += np.expand_dims(tfac1.values,axis=1)*interm_var[:,tind_2,:].values
+                   
+                else:
+
+
+                    tfac1 = 1-(np.abs(model_data.time[f] - obs_data.time[tindex])/(model_data.time[1]-model_data.time[0]))
+                    
+                    ds[j][:,tindex,:] += np.expand_dims(tfac1.values,axis=1)*interm_var.values
+    return ds
+
+def omps_nm_pairing(model_data,obs_data,ozone_ppbv_varname):
+    'Pairs model ozone mixing ratio with OMPS nadir mapper retrievals. Calculates column without applying apriori'
+    import xarray as xr
+    import pandas as pd
+ 
+    print('pairing without applying averaging kernel')
+
+    if len(ozone_ppbv_varname) != 1:
+        print('ozone_ppbv_varname has more than one entry')
+
+    
+    du_fac = 1.0e-5*6.023e23/28.97/9.8/2.687e19 # conversion factor; moves model from ppbv to dobson
+    pair_variables = ['dp_pa',ozone_ppbv_varname]
+    paired_ds = space_and_time_pairing(model_data,obs_data,pair_variables)
+    
+    # calculate ozone column, no averaging kernel or apriori applied.
+    col = np.nansum(du_fac*(paired_ds['dp_pa']/100.)*paired_ds['o3vmr'],axis=0) # new dimensions will be (satellite_x, satellite_y)
+    ds = xr.Dataset({ozone_ppbv_varname[0]: (['time','y'],col),
+                     'ozone_column':(['time','y'],obs_data.ozone_column.values)
+                               },
+                    coords={
+                        'longitude':(['time','y'],obs_data['longitude'].values),
+                        'latitude':(['time','y'],obs_data['latitude'].values),
+                        'time':(['time'],obs_data.time.values),
+                    })    
+
+    return ds
+                                                                            
+                                                                            
+
+def omps_nm_pairing_apriori(model_data,obs_data,ozone_ppbv_varname):
+    'Pairs model ozone mixing ratio data with OMPS nm. Applies satellite apriori column to model observations.'
+    import xarray as xr
+    import pandas as pd
+    try:
+        import xesmf as xe
+    except ImportError as e:
+        print('satellite_utilities: xesmf module not found')
+        raise
+
+    du_fac = 1.0e-5*6.023e23/28.97/9.8/2.687e19 # conversion factor; moves model from ppbv to dobson
+    
+    print('pairing with averaging kernel application')
+                     
+    # Grab necessary shape information
+    nf,nz_m,nx_m,ny_m = model_data[ozone_ppbv_varname[0]].shape
+    nx,ny = obs_data.ozone_column.shape
+    ## initialize intermediates for use in calcluating column
+    pressure_temp = np.zeros((nz_m,nx,ny))
+    ozone_temp = np.zeros((nz_m,nx,ny))
+    sfc = np.zeros((nx,ny))
+    ## loop over model time steps
+    for f in range(nf):
+        
+        tindex = np.where(np.abs(obs_data.time - model_data.time[f]) <= (model_data.time[1]-model_data.time[0]))[0]
+        if len(tindex):
+            # regrid spatially (model lat/lon to satellite swath lat/lon)
+            regridr = xe.Regridder(model_data.isel(time=f),obs_data[['latitude','longitude']].sel(x=tindex),'bilinear')
+            regrid_oz = regridr(model_data[ozone_ppbv_varname[0]][f])
+            regrid_p = regridr(model_data['pres_pa_mid'][f]) # this one should be pressure variable (for the interpolation).
+            sfp = regridr(model_data['surfpres_pa'][f])
+            # fixes for observations before/after model time range.
+            if f == (nf-1):
+                t2 = np.where((obs_data.time[tindex] >= model_data.time[f]))[0]
+                ozone_temp[:,tindex[t2],:] = regrid_oz[:,t2,:].values
+                pressure_temp[:,tindex[t2],:] = regrid_p[:,t2,:].values
+                sfc[t2,:] = sfp[t2,:].values 
+                tind_2 = np.where((obs_data.time[tindex] < model_data.time[f]) & 
+                                  (np.abs(obs_data.time[tindex] - model_data.time[f]) <= (model_data.time[1]-model_data.time[0])))[0]
+                tfac1 = 1-(np.abs(model_data.time[f] - obs_data.time[tindex[tind_2]])/(model_data.time[1]-model_data.time[0]))
+
+                ozone_temp[:,tindex[tind_2],:] += np.expand_dims(tfac1.values,axis=1)*regrid_oz[:,tind_2,:].values
+                pressure_temp[:,tindex[tind_2],:] += np.expand_dims(tfac1.values,axis=1)*regrid_p[:,tind_2,:].values
+                sfc[tindex[tind_2],:] += np.expand_dims(tfac1.values,axis=1)*sfp[tind_2,:].values
+            elif f == 0:
+                t2 = np.where((obs_data.time[tindex] <= model_data.time[f]))[0]
+                ozone_temp[:,tindex[t2],:] = regrid_oz[:,t2,:].values
+                pressure_temp[:,tindex[t2],:] = regrid_p[:,t2,:].values
+                sfc[tindex[t2],:] = sfp[t2,:].values 
+                tind_2 = np.where((obs_data.time[tindex] > model_data.time[f]) & 
+                                  (np.abs(obs_data.time[tindex] - model_data.time[f]) <= (model_data.time[1]-model_data.time[0])))[0]
+                tfac1 = 1-(np.abs(model_data.time[f] - obs_data.time[tindex[tind_2]])/(model_data.time[1]-model_data.time[0]))
+                ozone_temp[:,tindex[tind_2],:] += np.expand_dims(tfac1.values,axis=1)*regrid_oz[:,tind_2,:].values
+                pressure_temp[:,tindex[tind_2],:] += np.expand_dims(tfac1.values,axis=1)*regrid_p[:,tind_2,:].values
+                sfc[tind_2,:] += np.expand_dims(tfac1.values,axis=1)*sfp[tind_2,:].values
+            else:
+                tfac1 = 1-(np.abs(model_data.time[f] - obs_data.time[tindex])/(model_data.time[1]-model_data.time[0]))
+                ozone_temp[:,tindex,:] += np.expand_dims(tfac1.values,axis=1)*regrid_oz.values
+                pressure_temp[:,tindex,:] += np.expand_dims(tfac1.values,axis=1)*regrid_p.values
+                sfc[tindex,:] += np.expand_dims(tfac1.values,axis=1)*sfp.values
+    # Interpolate model data to satellite pressure levels
+    from wrf import interplevel
+    # note: for interpolation in pressure coordinates to work, z dimension must be such that the smallest 
+    # pressure is on the bottom. With Melodies-Monet model datasets, this requires flipping the z dimension 
+    # as the model readers are set up to ensure the surface is at index 0. 
+    ozone_satp = interplevel(ozone_temp[::-1],pressure_temp[::-1]/100.,obs_data.pressure,missing=np.nan)
+    ozone_satp = ozone_satp.values
+    
+    ozone_satp[np.isnan(ozone_satp)] = 0
+    oz = np.zeros_like(obs_data.ozone_column.values)
+    
+    nl,n1,n2 = ozone_satp.shape
+    
+    # delta pressure calculation for satellite pressure midlevels
+    p = obs_data.pressure.values
+    shift_down = np.roll(p,-1)
+    shift_down[-1] =0
+
+    shift_up = np.roll(p,1)
+    band = (shift_up-p)/2+(p-shift_down)/2
+   
+    band[0] = (p-shift_down)[0]/2
+
+    band[-1] = (shift_up-p)[-1]/2 + (p-shift_down)[-1]
+    for i in range(nl):
+        
+        if i != 0:
+            dp = band[i]
+        else:
+            sfc[sfc == 0] = np.nan
+            dp = np.abs(sfc/100. - obs_data.pressure[i].values) + band[i]
+
+        add = du_fac*dp*ozone_satp[i]
+        eff = obs_data.layer_efficiency[:,:,i].values
+        ap = obs_data.apriori[:,:,i].values
+        oz = oz + ap*(1-eff) + (eff)*(add)
+ 
+    ds = xr.Dataset({ozone_ppbv_varname[0]: (['time','y'],oz),
+                     'ozone_column':(['time','y'],obs_data.ozone_column.values)
+                               },
+                    coords={
+                        'longitude':(['time','y'],obs_data['longitude'].values),
+                        'latitude':(['time','y'],obs_data['latitude'].values),
+                        'time':(['time'],obs_data.time.values),
+                    })
+    return ds
diff --git a/melodies_monet/util/time_interval_subset.py b/melodies_monet/util/time_interval_subset.py
new file mode 100644
index 00000000..118465ed
--- /dev/null
+++ b/melodies_monet/util/time_interval_subset.py
@@ -0,0 +1,37 @@
+# Copyright (C) 2022 National Center for Atmospheric Research and National Oceanic and Atmospheric Administration
+# SPDX-License-Identifier: Apache-2.0
+#
+def subset_model_filelist(all_files,timeformat,timestep,timeinterval):
+    '''Subset model filelist to within a given time interval. 
+    Filename requirements:
+    - individual files for each timestep
+    - time must be in filename
+    '''
+    import pandas as pd
+    subset_interval = pd.date_range(start=timeinterval[0],end=timeinterval[-1],freq=timestep)
+    interval_files = []
+    for i in subset_interval:
+        flst = [fs for fs in all_files if i.strftime(timeformat) in fs]
+        if len(flst) == 1:
+            interval_files.append(flst[0])
+        elif len(flst) >1:
+            print('More than 1 file for {} in listing'.format(i.strftime(timeformat)))
+    return interval_files
+
+def subset_OMPS_l2(file_path,timeinterval):
+    '''Dependent on filenaming convention
+    '''
+    import pandas as pd
+    from glob import glob
+    import fnmatch
+    all_files = glob(file_path)
+    interval_files = []
+    subset_interval = pd.date_range(start=timeinterval[0],end=timeinterval[-1],freq='D',inclusive='left')
+    
+    for i in subset_interval:
+        fst = fnmatch.filter(all_files,'*OMPS-NPP_NMTO3-L2_v*_{}*_o*'.format(i.strftime('%Ym%m%d')))
+        fst.sort()
+        for j in fst:
+            interval_files.append(j)
+    return interval_files
+    
diff --git a/to_generalize/OutputPlot_Config.py b/to_generalize/OutputPlot_Config.py
new file mode 100755
index 00000000..9bb3b2d5
--- /dev/null
+++ b/to_generalize/OutputPlot_Config.py
@@ -0,0 +1,353 @@
+
+# This code is written to process monthly averaged map for both wrfchem and TROPOMI
+# --- Meng Li, 2019. 5. 9
+# --- Contact: meng.li@noaa.gov; meng.li.atm@gmail.com
+
+import os
+import numpy as np
+from netCDF4 import Dataset
+import matplotlib.pyplot as plt
+import wrf
+from os import listdir
+from os.path import isfile
+
+
+Basedir_wrfoutput = os.environ.get('Basedir_wrfoutput')
+
+'''
+===================================================
+Main program: wrfoutput, tropomi, and evaluation
+===================================================
+'''
+
+#=================Preparation Codes==================
+#---
+class file_management:
+    def __init__(self):
+        pass
+    def subdirlist(self, indir, keyword=''):
+        subdirlist = []
+        subdirlist_org = [x[0] for x in os.walk(indir)]
+        for sd in subdirlist_org:
+            if keyword in sd:
+                subdirlist.append(sd)
+        return subdirlist
+                
+        return subdirlist
+    def filelist(self, indir, keyword=''):
+        filelist = []
+        filelist_org = [os.path.join(indir, f) for f in listdir(indir) if isfile(os.path.join(indir,f))]
+        for f in filelist_org:
+            if keyword in f:
+                filelist.append(f)
+        return filelist   
+    
+#---
+def extractwrfcoord(lats='', lons=''):
+    # extract one wrfdata
+    fm = file_management()
+    subdirlist = fm.subdirlist(Basedir_wrfoutput)
+    ff = fm.filelist(subdirlist[1])[0]
+    wrfin = Dataset(ff,'r',format = 'NETCDF4_CLASSIC')
+        
+    # get some attributes of the wrf domain 
+    latdata = wrf.getvar(wrfin, 'XLAT', timeidx=0)[:,:]            # latitude
+    londata = wrf.getvar(wrfin, 'XLONG', timeidx=0)[:,:]           # longitude
+    wrflonlat = {'lon':londata, 'lat':latdata}
+
+    if (lats == '') & (lons == ''):
+        return wrflonlat
+    else:
+        xyinds = wrf.ll_to_xy(wrfin, lats, lons)
+        return xyinds
+    wrfin.close()
+
+#===========MAIN PROGRAM STARTS HERE===============
+    
+# GET THE WRF COORDIATE INFORMATION
+wrfcoord = extractwrfcoord()
+#---
+class output_config:   
+    def __init__(self):
+        SMALL_SIZE=12
+        MEDIUM_SIZE = 16
+        BIG_SIZE = 18
+        plt.rc('font', size=SMALL_SIZE )
+        plt.rc('axes', titlesize=SMALL_SIZE)
+        plt.rc('axes', labelsize=MEDIUM_SIZE)
+        plt.rc('xtick', labelsize=SMALL_SIZE)
+        plt.rc('ytick', labelsize=SMALL_SIZE)
+        plt.rc('legend', fontsize=MEDIUM_SIZE)
+        plt.rc('figure', titlesize=BIG_SIZE)
+        plt.rc('font',**{'family':'sans-serif','sans-serif':['arial']})
+
+        
+    def outputnc_2d(self, fn, value, valuename,valueunit):      
+        print('--> output 2d data for:', valuename)
+        ds = Dataset(fn, 'w', format = 'NETCDF4_CLASSIC')
+        ds.createDimension('longitude', np.shape(value)[1])
+        ds.createDimension('latitude', np.shape(value)[0])
+        dlong = ds.createVariable('longitude', 'f4', ['latitude','longitude'])
+        dlat = ds.createVariable('latitude', 'f4', ['latitude','longitude'])
+        dsec = ds.createVariable(valuename, 'f4', ['latitude','longitude'])
+    					
+        #lat, lon = wrf.latlon_coords(value)
+        lon = wrfcoord['lon']
+        lat = wrfcoord['lat']
+        ds.variables['longitude'][:,:] = lon[:,:]
+        ds.variables['latitude'][:,:] = lat[:,:]
+        ds.longitude = 'Edge of grids, West to East'
+        ds.latitude = 'Edge of grids, South to North'
+        ds.variables[valuename][:,:] = value[:,:]
+        ds.valuename = valueunit
+        ds.close()       
+    
+
+    def outputnc_3d(self, fn, lon,lat,time, valuename, value, valueunit):       
+    	ds = Dataset(fn, 'w', format = 'NETCDF4_CLASSIC')   
+    	ds.createDimension('longitude', np.shape(lon)[0])
+    	ds.createDimension('latitude', np.shape(lat)[0])
+    	ds.createDimension('time', np.shape(time)[0])
+    
+    	dlong = ds.createVariable('longitude', 'f4', ['longitude'])
+    	dlat = ds.createVariable('latitude', 'f4', ['latitude'])
+    	dmonth = ds.createVariable('time', 'f4', ['time'])	
+    	dsec = ds.createVariable(valuename, 'f4', ['time','latitude','longitude'])
+    					
+    	ds.variables['longitude'][:] = lon[:]
+    	ds.variables['latitude'][:] = lat[:]
+    	ds.variables['time'][:] = time[:]
+    	ds.longitude = 'Edge of grids, West to East'
+    	ds.latitude = 'Edge of grids, South to North'
+    	ds.month = 'Time'
+    
+    	ds.variables[valuename][:,:,:] = value[:,:,:]		
+    	ds.valuename = valueunit
+    	ds.close()
+        
+    def plot_2dmap(self, fn, value,valuename, valueunit,  mindata=0.0, maxdata = 0.0):    
+        print('--> plotting 2d map for:', valuename       )
+        from wrf import (to_np, get_cartopy, cartopy_xlim, cartopy_ylim, latlon_coords)
+        import cartopy.crs as crs        
+        from cartopy.feature import NaturalEarthFeature
+        from matplotlib.cm import get_cmap
+        from cartopy.io.shapereader import Reader
+        from cartopy.feature import ShapelyFeature
+        
+	# get the cartopy mapping object
+        cart_proj = get_cartopy(wrfcoord['lon'])
+        lats, lons = latlon_coords(wrfcoord['lon'])    
+        # create a figure
+        fig = plt.figure(figsize=(12,6))    
+        # set the GeoAxes to the projection used by WRF
+        ax = plt.axes(projection=cart_proj)
+        # download and add the states and coastlines
+        # states = NaturalEarthFeature(category="cultural",scale="50m",
+        #                             facecolor="none", name="admin_1_states_provinces_shp")
+        states_reader = Reader('/scratch1/BMC/rcm2/mli/tech/ne_50m_admin_1_states_provinces/ne_50m_admin_1_states_provinces.shp')
+        states = ShapelyFeature(states_reader.geometries(), crs.PlateCarree())
+        ax.add_feature(states,linewidth=0.5, edgecolor="black", facecolor='none')
+        #ax.coastlines('50m',linewidth=0.8)
+        coast_reader = Reader('/scratch1/BMC/rcm2/mli/tech/ne_50m_coastline/ne_50m_coastline.shp')
+        coast = ShapelyFeature(coast_reader.geometries(), crs.PlateCarree())
+        ax.add_feature(coast, linewidth=0.8, edgecolor='black', facecolor='none')
+
+        # make the contour outlines and filled contoures for the value
+        #plt.contour(lons, lats, value, 10, colors="black", transform=crs.PlateCarree())
+        #plt.contourf(lons, lats, value, vmin=mindata, vmax=maxdata, transform=crs.PlateCarree(), cmap='jet')
+        if (mindata == 0.0 and maxdata == 0.0):
+            mindata = np.min(value)
+            maxdata = np.max(value)
+            
+        plt.pcolormesh(lons, lats, value, vmin = mindata, vmax = maxdata, cmap='jet',transform=crs.PlateCarree() )
+        #plt.imshow(lons, lats, value)
+        cb = plt.colorbar(ax=ax, shrink=.98)
+        cb.ax.tick_params(labelsize=18, length=8)
+        # set the map bounds
+        ax.set_xlim(cartopy_xlim(wrfcoord['lon']))
+        ax.set_ylim(cartopy_ylim(wrfcoord['lat']))
+        
+        # ad the grid lines
+        ax.gridlines(color="black", linestyle = "dotted")
+        plt.title(valuename + ', unit: ' +valueunit, fontsize=22, fontweight='bold')
+        plt.savefig(fn, dpi=300)
+        #plt.show()
+        plt.clf()
+        plt.close()
+
+    def plot_2dmap_ccolbar(self, fn, value,valuename, valueunit,  mindata=0.0, maxdata = 0.0):
+        print('--> plotting 2d map for:', valuename)
+        from wrf import (to_np, get_cartopy, cartopy_xlim, cartopy_ylim, latlon_coords)
+        import cartopy.crs as crs
+        from cartopy.feature import NaturalEarthFeature
+        from matplotlib.cm import get_cmap
+        import matplotlib as mpl
+        from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
+        from cartopy.io.shapereader import Reader
+        from cartopy.feature import ShapelyFeature
+
+        # get the cartopy mapping object
+        cart_proj = get_cartopy(wrfcoord['lon'])
+        lats, lons = latlon_coords(wrfcoord['lon'])
+        # create a figure
+        fig = plt.figure(figsize=(12,6))
+        # set the GeoAxes to the projection used by WRF
+        ax = plt.axes(projection=cart_proj)
+        # download and add the states and coastlines
+        states_reader = Reader('/scratch1/BMC/rcm2/mli/tech/ne_50m_admin_1_states_provinces/ne_50m_admin_1_states_provinces.shp')
+        states = ShapelyFeature(states_reader.geometries(), crs.PlateCarree())
+        ax.add_feature(states,linewidth=0.5, edgecolor="black", facecolor='none')
+        #ax.coastlines('50m',linewidth=0.8)
+        coast_reader = Reader('/scratch1/BMC/rcm2/mli/tech/ne_50m_coastline/ne_50m_coastline.shp')
+        coast = ShapelyFeature(coast_reader.geometries(), crs.PlateCarree())
+        ax.add_feature(coast, linewidth=0.8, edgecolor='black', facecolor='none')
+
+        # make the contour outlines and filled contoures for the value
+        #plt.contour(lons, lats, value, 10, colors="black", transform=crs.PlateCarree())
+        #plt.contourf(lons, lats, value, vmin=mindata, vmax=maxdata, transform=crs.PlateCarree(), cmap='jet')
+        if (mindata == 0.0 and maxdata == 0.0):
+            mindata = np.min(value)
+            maxdata = np.max(value)
+        # define a custom colorbar
+        #cmap = plt.cm.RdBu_r
+        #cmap = plt.get_cmap('bwr')
+        cmap = plt.get_cmap('PuBuGn')
+        # extract all colors from the .jet map
+        cmaplist = [cmap(i) for i in range(cmap.N)]
+        # CREATE THE NEW MAP
+        cmap = mpl.colors.LinearSegmentedColormap.from_list('Custom cmap', cmaplist, cmap.N)
+        # define the bins and normalize
+        #bounds = np.linspace(0,20, 21)
+        bounds = np.linspace(mindata,maxdata,11)
+        norm = mpl.colors.BoundaryNorm(boundaries=bounds, ncolors=cmap.N)
+
+        plt.pcolormesh(lons, lats, value, vmin = mindata, vmax = maxdata, norm=norm,cmap=cmap,transform=crs.PlateCarree() )
+        #plt.imshow(lons, lats, value)
+        cb = plt.colorbar(ax=ax, shrink=.98,extend='both', ticks=bounds)
+        cb.ax.tick_params(labelsize=18, length=8)
+        # set the map bounds
+        #ax.set_xlim(cartopy_xlim(wrfcoord['lon']))
+        #ax.set_ylim(cartopy_ylim(wrfcoord['lat']))
+        ax.set_xlim(cartopy_xlim(wrfcoord['lon']))
+        ax.set_ylim(cartopy_ylim(wrfcoord['lat']))
+
+        # ad the grid lines
+        ax.gridlines(color="black", linestyle = "dotted")
+        plt.title(valuename + ', unit: ' +valueunit, fontsize=22, fontweight='bold' )
+        plt.savefig(fn, dpi=300)
+        #plt.show()
+        plt.clf()
+        plt.close()
+
+    def plot_scatter(self,fn, title, x, y, mindata=0.0, maxdata=1e17, mb=None, nmb=None, label='$\mathregular{NO_2}$ column',
+            xlabel='TROPOMI $\mathregular{NO_2}$ column, $\mathregular{10^{15}}$ molec c$\mathregular{m^{-2}}$',
+            ylabel='WRF-Chem $\mathregular{NO_2}$ column, $\mathregular{10^{15}}$ molec c$\mathregular{m^{-2}}$'):
+        from scipy import stats
+        #fig = plt.figure(figsize=(6,6))
+        fig, ax = plt.subplots(figsize=(6,6))
+        x = x/1e15
+        y = y/1e15
+        mindata = mindata/1e15
+        maxdata = maxdata/1e15
+        slope, intercept, r_value, p_value, stderr=stats.linregress(x, y)
+        plt.scatter(x,y, marker = 'o',facecolors='cornflowerblue', edgecolors='b',label=label, s=150) # s=50
+        plt.xlim(mindata, maxdata)
+        plt.ylim(mindata, maxdata)
+        plt.xlabel(xlabel,fontsize=18, weight=500)
+        plt.ylabel(ylabel,fontsize=18, weight=500)
+
+        xarr = np.arange(start=0.0, stop=1e17,step=1e15)
+        plt.plot(xarr, xarr,'k--',label='_nolegend')
+
+        y2 = xarr*slope + intercept
+        plt.plot(xarr, y2, 'r-',label='liner regression')
+        div = (maxdata - mindata)/18.0
+
+        if intercept > 0.0:
+            eqstr = 'Y='+'{:5.2f}'.format(slope)+'X+'+'{:5.2f}'.format(intercept)+'e15'#'$\mathregular{10^{15}}$'
+        else:
+            eqstr = 'Y='+'{:5.2f}'.format(slope)+'X-'+'{:5.2f}'.format(intercept*(-1.0))+'e15'
+        plt.text(maxdata/2.0, mindata+div*5.0,eqstr,color='r',fontname = 'arial',fontsize=18) # fontweight='bold', fontstyle='italic'
+        r_value = r_value * r_value
+        rstr = '$\mathregular{R^{2}}$:'+'{:5.2f}'.format(r_value)
+        plt.text(maxdata/2.0, mindata+div*3.5,rstr,color='r',fontsize=18)
+
+        plt.text(maxdata*0.2/6.0, maxdata-div*5.0, 'N: ' + str(len(x)),fontname='arial',fontsize=18)
+        #plt.text(maxdata/2.0, maxdata-div*2.0, 'slope: '+ '{:5.2f}'.format(slope), **csfont)
+        #plt.text(maxdata/2.0, maxdata-div*3.0, 'intercept: '+ '{:5.2f}'.format(intercept/1e15) + 'e15', **csfont)
+        if mb != None:
+            plt.text(maxdata*0.2/6.0, maxdata-div*6.5, 'MB: ' + '{:5.2f}'.format(mb/1e15)+'e15',fontname='arial', fontsize=18)
+        if nmb != None:
+            plt.text(maxdata*0.2/6.0, maxdata-div*8.0, 'NMB: ' + '{:5.2f}'.format(nmb*100.0)+'%',fontname='arial', fontsize=18)
+ 
+        plt.legend(loc='upper left',fontsize=16)
+        #plt.title(title,fontname='arial', fontsize=16)
+        ax.tick_params(length=8, width=1, labelsize=18)
+        plt.savefig(fn, bbox_inches = 'tight', dpi=300)
+        #plt.show()
+        plt.clf()
+        plt.close()
+        print('--> scatter: r,slope,intercept:', r_value, slope, intercept/1e15)
+        
+    def plot_minus(self, fn, value,valuename, valueunit,  mindata=-1.0e16, maxdata = 1.0e16):
+        print('--> plotting 2d minus map for:', valuename)
+        
+        from matplotlib import cm
+        from wrf import (to_np, get_cartopy, cartopy_xlim, cartopy_ylim, latlon_coords)
+        import cartopy.crs as crs
+        from cartopy.feature import NaturalEarthFeature
+        from matplotlib.cm import get_cmap
+        from cartopy.io.shapereader import Reader
+        from cartopy.feature import ShapelyFeature       
+ 
+        # get the cartopy mapping object
+        cart_proj = get_cartopy(wrfcoord['lon'])
+        lats, lons = latlon_coords(wrfcoord['lon'])    
+        # create a figure
+        fig = plt.figure(figsize=(12,6))    
+        # set the GeoAxes to the projection used by WRF
+        ax = plt.axes(projection=cart_proj)
+        # download and add the states and coastlines
+        # states = NaturalEarthFeature(category="cultural",scale="50m",
+        #                             facecolor="none", name="admin_1_states_provinces_shp")
+        states_reader = Reader('/scratch1/BMC/rcm2/mli/tech/ne_50m_admin_1_states_provinces/ne_50m_admin_1_states_provinces.shp')
+        states = ShapelyFeature(states_reader.geometries(), crs.PlateCarree())
+        ax.add_feature(states,linewidth=0.5, edgecolor="black",facecolor='none')
+        #ax.coastlines('50m',linewidth=0.8)
+        coast_reader = Reader('/scratch1/BMC/rcm2/mli/tech/ne_50m_coastline/ne_50m_coastline.shp')
+        coast = ShapelyFeature(coast_reader.geometries(), crs.PlateCarree())
+        ax.add_feature(coast, linewidth=0.8, edgecolor='black', facecolor='none')
+
+        # make the contour outlines and filled contoures for the value
+        #plt.contour(lons, lats, value, 10, colors="black", transform=crs.PlateCarree())
+        #plt.contourf(lons, lats, value, vmin=mindata, vmax=maxdata, transform=crs.PlateCarree(), cmap='jet')
+        if (mindata == 0.0 and maxdata == 0.0):
+            mindata = np.min(value)
+            maxdata = np.max(value)
+        
+        cmap = plt.get_cmap('bwr')
+        plt.pcolormesh(lons, lats, value, vmin = mindata, vmax = maxdata, cmap=cmap,transform=crs.PlateCarree() )       
+        # color bar
+        cb = plt.colorbar(ax=ax, shrink=.98)
+        cb.ax.tick_params(labelsize=18, length=8)
+        # set the map bounds
+        ax.set_xlim(cartopy_xlim(wrfcoord['lon']))
+        ax.set_ylim(cartopy_ylim(wrfcoord['lat']))
+        
+        # ad the grid lines
+        ax.gridlines(color="black", linestyle = "dotted")
+        plt.title(valuename + ', unit: ' +valueunit, fontsize=22, fontweight='bold')
+        plt.savefig(fn, dpi=300)
+        #plt.show()
+        plt.clf()
+        plt.close()
+        
+
+#---
+if __name__ == '__main__':
+        main()
+    
+ 
+
+
+
diff --git a/to_generalize/avg_trp_no2.py b/to_generalize/avg_trp_no2.py
new file mode 100644
index 00000000..fd53b61e
--- /dev/null
+++ b/to_generalize/avg_trp_no2.py
@@ -0,0 +1,281 @@
+
+# This code is written to process monthly averaged map for both wrfchem and TROPOMI
+# --- Meng Li, 2019. 5. 9
+# --- Contact: meng.li@noaa.gov; meng.li.atm@gmail.com
+
+import os
+import numpy as np
+from netCDF4 import Dataset
+import wrf
+from os import listdir
+from os.path import isfile
+
+from OutputPlot_Config import output_config
+
+# baseline + lightning
+Basedir_wrfoutput = '/scratch1/BMC/rcm2/mli/nyc18_lightning/run_12km_five18_bmcdVCP_fog_wofire_BEIS_0.5ISO/Output/'
+Baseoutdir = '/scratch1/BMC/rcm2/mli/outdir_12km_noPM_baseline_bmc_cams/'
+
+year = 2018
+seasoname = 'm07'
+
+'''
+====================================================================
+Comparison Between TROPOMI and WRF-Chem NO2 Trop. Columns Seasonaly
+====================================================================
+'''
+
+Month = [7]
+MonthStartDay = [1] # start day for each month, 1-based
+MonthEndDay = [15]  # end day for each month, 1-based
+
+
+# Use wind speed as a criterion?
+UseWPD = False  # False: not use wind speed
+
+#================Preparation Codes======================
+#---
+class file_management:
+    def __init__(self):
+        pass
+    def subdirlist(self, indir, keyword=''):
+        subdirlist = []
+        print(indir)
+        subdirlist_org = [x[0] for x in os.walk(indir)]
+        for sd in subdirlist_org:
+            if keyword in sd:
+                subdirlist.append(sd)
+        return subdirlist
+                
+        return subdirlist
+    def filelist(self, indir, keyword=''):
+        filelist = []
+        filelist_org = [os.path.join(indir, f) for f in listdir(indir) if isfile(os.path.join(indir,f))]
+        for f in filelist_org:
+            if keyword in f:
+                filelist.append(f)
+        return filelist   
+    
+#---
+def extractwrfcoord(lats='', lons=''):
+    # extract one wrfdata
+    fm = file_management()
+    subdirlist = fm.subdirlist(Basedir_wrfoutput)
+    ff = fm.filelist(subdirlist[1])[0]
+    wrfin = Dataset(ff,'r',format = 'NETCDF4_CLASSIC')
+        
+    # get some attributes of the wrf domain 
+    latdata = wrf.getvar(wrfin, 'XLAT', timeidx=0)[:,:]            # latitude
+    londata = wrf.getvar(wrfin, 'XLONG', timeidx=0)[:,:]           # longitude
+    wrflonlat = {'lon':londata, 'lat':latdata}
+
+    if (lats == '') & (lons == ''):
+        return wrflonlat
+    else:
+        xyinds = wrf.ll_to_xy(wrfin, lats, lons)
+        return xyinds
+    wrfin.close()
+
+#==============MAIN PROGRAM STARTS HERE==============
+    
+# GET THE WRF COORDIATE INFORMATION OF WRF-CHEM
+wrfcoord = extractwrfcoord()
+wrflonlat = wrfcoord
+# extract locations
+wrflon = wrflonlat['lon']
+wrflat = wrflonlat['lat']
+xy = np.shape(wrflonlat['lon'])
+ 
+# MAIN PROGRAM
+def main():
+    m = model_validation(year)
+    m.evaluatedata()
+        
+class model_validation():
+    
+    def __init__(self, year):        
+        self.year = year
+        
+    def evaluatedata(self):
+        year = self.year
+        
+        # define the outdir based on use wind speed or not
+        if UseWPD == False:
+            Outdir = Baseoutdir + seasoname + '/'
+        else:
+            Outdir = Baseoutdir + seasoname + '/' + 'wpduvle'+str(maxwd) + '/'
+        if os.path.isdir(Outdir):
+            pass
+        else:
+            os.mkdir(Outdir)
+        print('***Evaluation starts here: ', year, seasoname)
+      
+        # initialize data array
+        no2_tomi = np.zeros([xy[0], xy[1]], dtype = np.float32) #TROPOMI NO2 columns for further sum
+        no2_tomi[:,:] = 0.0       
+        no2_wrfchem = np.zeros([xy[0], xy[1]], dtype = np.float32) #WRF-Chem NO2 columns for further sum
+        no2_wrfchem[:,:] = 0.0
+        
+        num_tomi = np.zeros([xy[0], xy[1]], dtype = np.float32) #Number of observations
+        num_tomi[:,:] = 0.0        
+        num_wrfchem = np.zeros([xy[0], xy[1]], dtype = np.float32) #Number of observations or WRF-Chem, should be the same
+        num_wrfchem[:,:] = 0.0
+
+        no2_tomi_avg = np.zeros([xy[0], xy[1]], dtype = np.float32) #seasonal averaged TROPOMI NO2 columns
+        no2_tomi_avg[:,:] = np.nan       
+        no2_wrfchem_avg = np.zeros([xy[0], xy[1]], dtype = np.float32) #seasonal averaged WRF-Chem NO2 columns
+        no2_wrfchem_avg[:,:] = np.nan
+        
+        # summerize each day
+        for mind in range(len(Month)):
+            month = Month[mind]
+            daymin = MonthStartDay[mind]
+            daymax = MonthEndDay[mind]
+
+            for day in range(daymin,daymax+1):
+
+                Indir = Baseoutdir + '{:02d}'.format(month) + '{:02d}'.format(day)+'/'
+                fn = Indir+ 'no2_wrfchem_'+str(year)+'_'+str(month)+'_'+str(day)+'.nc'
+                if isfile(fn):
+                    # wrfchem daily no2 column 
+                    fn = Indir+ 'no2_wrfchem_'+str(year)+'_'+str(month)+'_'+str(day)+'.nc'
+                    print('--> reading wrfchem datafile of : ', fn)
+                    ds = Dataset(fn,"r")
+                    variable_wc = ds.variables['NO2'][:,:]
+                    ds.close()
+
+                    # tropomi daily no2 column
+                    fn = Indir+ 'no2_tropomi_wchamf_'+str(year)+'_'+str(month)+'_'+str(day)+'.nc'
+                    print('--> reading tropomi datafile of : ', fn)
+                    ds = Dataset(fn,"r")
+                    variable_tp = ds.variables['NO2'][:,:]
+                    ds.close() 
+
+                    if UseWPD == False:
+                        # add to summary array
+                        ind = np.where((variable_wc >= 0.0) & (variable_tp >= 0.0) & (variable_tp != np.nan))
+                        print('check', ind)
+                        print('wc',np.nanmin(variable_wc), np.nanmax(variable_wc))
+                        print('wc',np.nanmin(variable_tp), np.nanmax(variable_tp))
+                        no2_wrfchem[ind] += variable_wc[ind]
+                        num_wrfchem[ind] += 1.0
+                
+                        #ind = np.where(variable_tp >= 0.0)
+                        no2_tomi[ind] += variable_tp[ind]
+                        num_tomi[ind] += 1.0
+
+                    else:
+			            # read the surface wind speed file
+                        fnwpd_u = Indir + 'wrfchem_u10_'+str(year)+'{:02d}'.format(month)+'{:02d}'.format(day)+'.nc'
+                        ds = Dataset(fnwpd_u,"r")
+                        variable_wpdu = ds.variables['u10'][:,:]
+                        print('--> reading wrfchem u10 of :', fnwpd_u)
+                        print('    ',np.nanmin(variable_wpdu), np.nanmax(variable_wpdu))
+                        ds.close()
+                
+                        fnwpd_v = Indir + 'wrfchem_v10_'+str(year)+'{:02d}'.format(month)+'{:02d}'.format(day)+'.nc'
+                        ds = Dataset(fnwpd_v,"r")
+                        variable_wpdv = ds.variables['v10'][:,:]
+                        print('--> reading wrfchem u10 of :', fnwpd_v)
+                        print('    ', np.nanmin(variable_wpdv), np.nanmax(variable_wpdv))
+                        ds.close()
+                        # the surface wind speed
+                        wd = (variable_wpdu**2 + variable_wpdv**2)**0.5
+
+                        # add to summary array
+                        ind = np.where((variable_wc >= 0.0) & (np.absolute(wd) <= maxwd) & (variable_tp >= 0.0))
+                        no2_wrfchem[ind] += variable_wc[ind]
+                        num_wrfchem[ind] += 1.0
+                
+                        #ind = np.where((variable_tp >= 0.0) & (np.absolute(wd) <= maxwd))
+                        no2_tomi[ind] += variable_tp[ind]
+                        num_tomi[ind] += 1.0
+                else:
+                    print('--> NO wrfchem / tropomi file found: ', day, fn)
+                    pass
+
+        # calculate seasonal average
+        no2_tomi_avg = no2_tomi / num_tomi
+        no2_wrfchem_avg = no2_wrfchem / num_wrfchem
+
+        #----Output and Plotting ---
+        ot = output_config()
+        pmin = 0.0	# pcolormap, min
+        pmax = 1e16	# pcolormap, max
+     
+        # TROPOMI NO2 for comparison 
+        fnnc = Outdir+ 'no2_tropomi_wchamf_'+str(year)+'_'+ seasoname +'_'+'mavg'+'.nc'
+        ot.outputnc_2d(fnnc, no2_tomi_avg, 'NO2', 'molec cm-2')
+
+        fn = Outdir+ 'no2_tropomi_wchamf_'+str(year)+'_'+ seasoname +'_'+'mavg'
+        ot.plot_2dmap(fn, no2_tomi_avg,'NO2','molec cm-2', mindata=pmin, maxdata = pmax)
+
+        print('TROPOMI NO2 after AMF revision:')
+        print('    x1e15: min ',np.nanmin(no2_tomi_avg)/1e15, ' max ',np.nanmax(no2_tomi_avg)/1e15, ' median ', np.nanmedian(no2_tomi_avg)/1e15, ' mean ',np.nanmean(no2_tomi_avg)/1e15)
+              
+        # WRF-Chem NO2 for comparison
+        fnnc = Outdir+ 'no2_wrfchem_'+str(year)+'_'+ seasoname +'_'+'mavg'+'.nc'
+        ot.outputnc_2d(fnnc, no2_wrfchem_avg, 'NO2', 'molec cm-2')
+
+        fn = Outdir+ 'no2_wrfchem_'+str(year)+'_'+ seasoname +'_'+ 'mavg'
+        ot.plot_2dmap(fn, no2_wrfchem_avg,'NO2','molec cm-2', mindata=pmin, maxdata = pmax)
+
+        print('WRF-Chem NO2:')
+        print('    x1e15: min ',np.nanmin(no2_wrfchem_avg)/1e15, ' max ',np.nanmax(no2_wrfchem_avg)/1e15, ' median ', np.nanmedian(no2_wrfchem_avg)/1e15, ' mean ',np.nanmean(no2_wrfchem_avg)/1e15               )
+
+        # Observation numbers
+        fnnc = Outdir+ 'num_tomi_no2_'+str(year)+'_'+ seasoname +'_'+'mavg'+'.nc'
+        ot.outputnc_2d(fnnc, num_tomi, 'number', 'unitless')
+
+        # Correlations
+        cal = calstatis()
+        ind = np.where((no2_tomi_avg > 0.0) & (no2_wrfchem_avg > 0.0))
+        x = no2_tomi_avg[ind].flatten()
+        y = no2_wrfchem_avg[ind].flatten()
+        mb = cal.calmb(y,x)
+        nmb = cal.calnmb(y,x)
+        fn = Outdir + 'corrl_trop_wrfchem_no2_'+str(year)+'_'+ seasoname +'_'+'mavg'
+        ot.plot_scatter(fn, 'NO2 columns', x, y, mindata=0.0, maxdata=3e16, mb=mb, nmb=nmb)
+
+        # minus
+        minarr = np.zeros([xy[0], xy[1]], dtype=np.float)
+        minarr[:,:] = np.nan
+        minarr[:,:] = (no2_wrfchem_avg[:,:] - no2_tomi_avg[:,:])/1e15
+        fn = Outdir + 'minus_wrfchem-tropomi_'+str(year)+'_'+seasoname + '_'+'mavg'
+        ot.plot_minus(fn,  minarr, 'NO2', '$\mathregular{10^{15}}$ molec c$\mathregular{m^{-2}}$',  mindata=-3.0, maxdata = 3.0)
+        
+
+
+class calstatis:
+    def __init__(self):
+        pass
+    def calcorrelation(self,modellist, obslist):
+        #r = np.corrcoef(x=modellist, y=obslist)
+        slope, intercept, r, p_value, stderr=stats.linregress(obslist, modellist)
+        return r
+    def calmb(self, modellist, obslist):
+        minuslist = [(modellist[n] - obslist[n]) for n in range(len(modellist))]
+        mb = np.sum(minuslist) / len(modellist)
+        return mb
+    def calrmse(self, modellist, obslist):
+        minuslist = [pow((modellist[n] - obslist[n]),2) for n in range(len(modellist))]
+        rmse = pow((np.sum(minuslist) / len(modellist)),0.5)
+        return rmse
+    def calnmb(self, modellist, obslist):
+        minuslist = [(modellist[n] - obslist[n]) for n in range(len(modellist))]
+        nmb = np.sum(minuslist) / np.sum(obslist)
+        return nmb
+    def calnme(self, modellist, obslist):
+        minuslist = [(abs(modellist[n] - obslist[n])) for n in range(len(modellist))]
+        nme = np.sum(minuslist) / np.sum(obslist)
+        return nme
+
+
+#---
+if __name__ == '__main__':
+        main()
+    
+ 
+
+
+
diff --git a/to_generalize/daily_trp_no2.py b/to_generalize/daily_trp_no2.py
new file mode 100755
index 00000000..56706496
--- /dev/null
+++ b/to_generalize/daily_trp_no2.py
@@ -0,0 +1,780 @@
+
+# This pro is written to read the wrf-chem output 
+# and process the TROPOMI data, to compare the observation with the model
+# --- Meng Li
+# --- 2019.04.11
+# --- Contact: meng.li@noaa.gov; meng.li.atm@gmail.com
+
+'''
+===================================================
+Import necessary packages and set the environment
+===================================================
+'''
+
+from os import listdir
+from os.path import isfile
+import csv, wrf,os,sys
+import numpy as np
+from netCDF4 import Dataset
+import multiprocessing
+from datetime import datetime
+import pytz
+from timezonefinder import TimezoneFinder
+import ESMF
+import xesmf as xe
+import math
+ESMF.Manager(debug=True)
+
+# import outputplot_config file
+from OutputPlot_Config import output_config
+
+
+# Get Basedir_tropomi, Baseoutdir, Basedir_wrfoutput, and Geofile from environment variables
+Basedir_tropomi = os.environ.get('Basedir_tropomi')
+Baseoutdir = os.environ.get('Baseoutdir')
+Basedir_wrfoutput = os.environ.get('Basedir_wrfoutput')
+Geofile = os.environ.get('Geofile')
+
+
+'''
+=============================================================
+Generate Daily Trop. NO2 Columns for TROPOMI and WRF-Chem
+=============================================================
+'''
+
+#==============Preparation Codes==================
+#---
+class file_management:
+    def __init__(self):
+        pass
+    def subdirlist(self, indir, keyword=''):
+        subdirlist = []
+        subdirlist_org = [x[0] for x in os.walk(indir)]
+        for sd in subdirlist_org:
+            if keyword in sd:
+                subdirlist.append(sd)
+        return subdirlist
+                
+        return subdirlist
+    def filelist(self, indir, keyword=''):
+        filelist = []
+        filelist_org = [os.path.join(indir, f) for f in listdir(indir) if isfile(os.path.join(indir,f))]
+        for f in filelist_org:
+            if keyword in f:
+                filelist.append(f)
+        return filelist   
+    
+#---
+
+def extractwrfcorners():
+    ds = Dataset(Geofile, "r")
+    variable_latc = ds['XLAT_C'][0,:,:]
+    variable_longc = ds['XLONG_C'][0,:,:] #1,285,441
+    cornerdic = {'lon_c': variable_longc, "lat_c": variable_latc}
+    #print(variable_latc)
+    ds.close()
+    return cornerdic
+
+def extractwrfcoord(lats=[''], lons=['']):
+    # extract one wrfdata
+    fm = file_management()
+    subdirlist = fm.subdirlist(Basedir_wrfoutput)
+    ff = fm.filelist(subdirlist[1])[0]
+    wrfin = Dataset(ff,'r',format = 'NETCDF4_CLASSIC')
+        
+    # get some attributes of the wrf domain 
+    latdata = wrf.getvar(wrfin, 'XLAT', timeidx=0)[:,:]            # latitude
+    londata = wrf.getvar(wrfin, 'XLONG', timeidx=0)[:,:]           # longitude
+    wrflonlat = {'lon':londata, 'lat':latdata}
+
+    if (len(lats) == 1) & (lats[0] == '') & (len(lons) == 1) & (lons[0] == ''):
+        cornerdic = extractwrfcorners()
+        wrflonlat.update(cornerdic)
+        #print(wrflonlat)
+        return wrflonlat
+    else:
+        xyinds = wrf.ll_to_xy(wrfin, lats, lons)
+        return xyinds
+    wrfin.close()
+
+
+#=======MAIN PROGRAM STARTS HERE=============
+    
+# GET THE WRF COORDIATE INFORMATION
+wrfcoord = extractwrfcoord()
+wrflon = wrfcoord['lon']
+wrflat = wrfcoord['lat']
+wrflon_c = wrfcoord['lon_c']
+wrflat_c = wrfcoord['lat_c']
+xy = np.shape(wrflon)
+tf = TimezoneFinder()
+
+print(wrflon_c)
+print(wrflat_c)
+
+# MAIN PROGRAM
+def main(year, month, day):
+    m = model_validation(year, month, day)
+    m.evaluatedata()
+    pass
+#---
+class model_validation():
+    
+    def __init__(self, year, month, day):        
+        self.year = year
+        self.month = month
+        self.day = day
+
+    
+    def findxy(self,coord_lons, coord_lats, lons, lats, iswrf):        
+        arrayout = extractwrfcoord(lats=lats, lons=lons)
+        arrayout = wrf.to_np(arrayout) # EDGE: coord_lons[0][0], coord_lats[0],[0], POINT OF (0,0)          
+        return arrayout
+
+    def extractloc(self, wrflon, wrflat, lons,lats):
+        londata = wrflon
+        latdata = wrflat
+        lats_1d = lats.flatten() # change 2-d to 1-d, dims of lons and lats should be the same
+        lons_1d  = lons.flatten() # change 2-d to 1-d       
+        idx = self.findxy(londata, latdata, lons_1d, lats_1d, 1)
+        idx = np.reshape(idx, [2, np.shape(lons)[0], np.shape(lons)[1]])
+        return idx
+        
+    def evaluatedata(self):
+        ot = output_config()
+        wrflonlat = wrfcoord
+        year = self.year
+        month = self.month
+        day = self.day
+
+        Outdir = Baseoutdir + '{:02d}'.format(month) + '{:02d}'.format(day)+'/'
+        # check if Outdir exsits, if not, create a new one.
+        if os.path.isdir(Outdir):
+            pass
+        else:
+            os.mkdir(Outdir)
+        print('*** Evaluation starts here: ', year, month, day)
+
+        # extract wrf-chem data directionary for each day
+        w = wrf_chem_process()
+        w.extractwrfdata(year, month, day)
+        wrf_omi_avg =  w.wrf_omi_avg
+
+        # no2 column and pressure of wrf-chem
+        wrfpres = wrf_omi_avg['pres']
+        wrfno2_omi_avg = wrf_omi_avg['no2']
+        wrfno2_omi_ppm = wrf_omi_avg['no2ppm']
+
+        # get the wrf grid cell center and boundaries
+        lat_wrf = wrflat
+        lon_wrf = wrflon
+        lat_wrf_b = wrflat_c
+        lon_wrf_b = wrflon_c
+        # extract the tropomi data dictionary including all swath tracks covering US
+        t = tropomi_process(year, month, day)
+        t.avgtropomi()
+        omidata_alltrack = t.omidata_alltrack
+        
+        print('--> total track number of tropomi is: '+str(len(omidata_alltrack)))
+        
+        # initialize arrays
+        no2colomi_avg = np.zeros([xy[0], xy[1], len(omidata_alltrack)], dtype = float) # NO2 column for sum in standard product 
+        no2colomi_avg[:,:,:] = np.nan
+
+        no2numomi_avg = np.zeros([xy[0], xy[1], len(omidata_alltrack)], dtype = float) # NO2 number for sum in standard product
+        no2numomi_avg[:,:,:] = 0.0
+
+        ratio = np.zeros([xy[0], xy[1], len(omidata_alltrack)], dtype = float) # ratio of revised column / standard column
+        ratio[:,:,:] = np.nan         
+
+        tamf_omi = np.zeros([xy[0], xy[1], len(omidata_alltrack)], dtype = float) # trop. AMF in standard product
+        tamf_omi[:,:,:] = np.nan         
+        
+        amf_model = np.zeros([xy[0], xy[1], len(omidata_alltrack)], dtype = float) # revised trop. AMF 
+        amf_model[:,:,:] = np.nan  
+
+        amf_total = np.zeros([xy[0], xy[1], len(omidata_alltrack)], dtype = float) # total AMF in standard product
+        amf_total[:,:,:] = np.nan
+
+        slantcol = np.zeros([xy[0], xy[1], len(omidata_alltrack)], dtype = float) # slant column density for sum in standard product
+        slantcol[:,:,:] = 0.0
+        
+        zmax_model = np.zeros([xy[0], xy[1]], dtype = np.int32) # vertical layer
+        zmax_model[:,:] = 0.0
+
+        no2_wrf_forcmp = np.zeros([xy[0], xy[1]], dtype = np.float) # final wrf-chem no2 column for comparison
+        no2_wrf_forcmp[:,:] = np.nan
+        
+        # processing each track into wrf domain and average 
+        for trk in range(len(omidata_alltrack)):
+            print('   -----> prosessing the track of ', trk)
+            omidata = omidata_alltrack[trk]
+            
+            # cell centers and boundaries
+            lon = np.squeeze(omidata['longitude'][:,:,:], axis=0)
+            lat = np.squeeze(omidata['latitude'][:,:,:], axis=0)     
+            lon_b = np.squeeze(omidata['longitude_bounds'][:,:,:,:], axis=0) # 3245x450x4
+            lat_b = np.squeeze(omidata['latitude_bounds'][:,:,:,:], axis=0)
+
+            locind = self.extractloc(wrflon, wrflat, lon, lat)  # locind: the value of x_wrf and y_wrf, omi domain shape
+
+            # extract data in standard NO2 product
+            no2colorg =np.squeeze(omidata['nitrogendioxide_tropospheric_column'][:,:,:], axis=0)# Trop. NO2 column in standard product
+            tnx = np.shape(lat)[0]
+            tny = np.shape(lat)[1]
+            lat_trp_b = np.zeros([tnx+1, tny+1], dtype = np.float)
+            lon_trp_b = np.zeros([tnx+1, tny+1], dtype = np.float)
+
+            # Revised NO2 Tropomi product at original dimension
+            no2colrev = np.zeros([tnx, tny],dtype=np.float)
+            no2colrev[:,:] = np.nan
+
+            qaorg = np.squeeze(omidata['qa_value'][:,:,:], axis=0)                              # quality flag
+            tamf_tm5 = np.squeeze(omidata['air_mass_factor_troposphere'][:,:,:], axis=0)        # TM5 trop. AMF 
+            amf_tm5 = np.squeeze(omidata['air_mass_factor_total'][:,:,:], axis=0)               # TM5 total AMF
+            cldfrc = np.squeeze(omidata['cloud_fraction_crb'][:,:,:],axis=0)                    # cloud fraction
+            tpreslev_tm5 = np.squeeze(omidata['preslev'][:,:,:], axis=0)                        # TM5 pressure level
+            trplayer_tm5 = np.squeeze(omidata['tm5_tropopause_layer_index'][:,:,:], axis=0)     # TM5 tropopause  
+            slant_col_single = np.squeeze(omidata['nitrogendioxide_slant_column_density'][:,:,:],axis=0)# NO2 Slant column density in standard product
+            scatwts = np.squeeze(omidata['averaging_kernel'][:,:,:,:],axis=0)                   # averaging kernel
+
+
+            for x_tomi in range(tnx):
+                for y_tomi in range(tny):
+                    x_wrf = locind[1,x_tomi, y_tomi] # MAX: 299, SOUTH-NORTH
+                    y_wrf = locind[0,x_tomi, y_tomi] # MIN: 239, WEST-EAST
+                    # get the cell boundaries of each swath
+                    lat_0 = lat_b[x_tomi,y_tomi,0]
+                    lat_1 = lat_b[x_tomi,y_tomi,1]
+                    lat_2 = lat_b[x_tomi,y_tomi,2]
+                    lat_3 = lat_b[x_tomi,y_tomi,3]
+
+                    lon_0 = lon_b[x_tomi,y_tomi,0]
+                    lon_1 = lon_b[x_tomi,y_tomi,1]
+                    lon_2 = lon_b[x_tomi,y_tomi,2]
+                    lon_3 = lon_b[x_tomi,y_tomi,3]
+
+                    lat_trp_b[x_tomi,y_tomi] = lat_0
+                    lat_trp_b[x_tomi,y_tomi+1] = lat_1
+                    lat_trp_b[x_tomi+1,y_tomi+1] = lat_2
+                    lat_trp_b[x_tomi+1,y_tomi] = lat_3
+
+                    lon_trp_b[x_tomi,y_tomi] = lon_0
+                    lon_trp_b[x_tomi,y_tomi+1] = lon_1
+                    lon_trp_b[x_tomi+1,y_tomi+1] = lon_2
+                    lon_trp_b[x_tomi+1,y_tomi] = lon_3
+   
+                    if (x_wrf < 0.0) or (x_wrf > xy[0]-1) or (y_wrf < 0.0) or (y_wrf > xy[1]-1):
+                        pass
+                    else:
+
+                        # extract the no2 trop column, quality, cloud radiation fraction                            
+                        value_tno2 = no2colorg[x_tomi, y_tomi]
+                        value_slnt = slant_col_single[x_tomi, y_tomi]
+                                            
+                        # screen data
+                        if qaorg[x_tomi, y_tomi] >= 0.75 and value_tno2 > 0.0e-30 and cldfrc[x_tomi, y_tomi] <= 0.5: # QUALITY CONTROL
+                            value_tno2 = self.converunit(value_tno2, 'mole m-2', 'molec cm-2') # CONVERT THE UNIT
+                            value_slnt = self.converunit(value_slnt, 'mole m-2', 'molec cm-2') # CONVERT THE UNIT
+
+                            # add slant NO2 column for further averaging
+                            no2numomi_avg[x_wrf, y_wrf, trk] += 1.0
+                            slantcol[x_wrf, y_wrf, trk] += value_slnt
+                            if value_slnt < 0.0:
+                                print('Error for slant column here!' )
+        
+                            # revise amf using wrfchem NO2 vertical profile, start here
+                            scatwts_vertical = scatwts[x_tomi, y_tomi, :]
+                            tpreslev = tpreslev_tm5[x_tomi, y_tomi,:]
+                            trplayer = trplayer_tm5[x_tomi, y_tomi]
+
+                            wrfpreslayer = wrfpres[:,x_wrf, y_wrf]
+                            wrfno2layer_molec = wrfno2_omi_avg[:,x_wrf, y_wrf] # mole cm^-2 by WRF layers
+                            wrfno2layer = wrfno2_omi_ppm[:,x_wrf, y_wrf] # use unit of ppm to derive NO2 profile
+
+                            # trop. AMF and total AMF in standard product
+                            tamf_org = tamf_tm5[x_tomi, y_tomi]    # trop. amf
+                            tamf_omi[x_wrf, y_wrf, trk] = tamf_org # add trop. amf to array
+                            amf_total[x_wrf, y_wrf, trk] = amf_tm5[x_tomi, y_tomi] # add total amf to array
+
+                         
+                            # find the vertical index of wrf-chem corresponding to the tropomi tropopause
+                            if type(trplayer) == np.int32:
+                                X = abs(wrfpreslayer - tpreslev[trplayer])
+                                zm_wrf = np.where(X == np.min(X))
+                                zmax_model[x_wrf, y_wrf] = zm_wrf[0][0]
+
+                                # calculate the revised trop. AMF, amf_model
+                                scatwts_vertical = scatwts_vertical * amf_total[x_wrf,y_wrf,trk] / tamf_org # converting from AKs to tropospheric AKs
+                                amf_model[x_wrf, y_wrf,trk] = self.calamfwrfchem(scatwts_vertical, wrfpreslayer, wrfno2layer, tpreslev, trplayer, zm_wrf[0][0], wrfno2layer_molec)*tamf_org
+
+                                # summarize all columns in WRF-Chem from surface to the tropopause
+                                ratio[x_wrf,y_wrf, trk] = tamf_org/amf_model[x_wrf, y_wrf, trk]
+                                
+                            else:
+                                #no2wrf_forcmp[x_wrf, y_wrf, trk] = np.nansum(wrfno2layer[:])
+                                ratio[x_wrf, y_wrf, trk] = 1.0
+
+
+                            no2colrev[x_tomi, y_tomi] = value_tno2*ratio[x_wrf,y_wrf,trk]
+            
+                        else:
+                            no2colrev[x_tomi, y_tomi] = np.nan
+
+            
+            # Regrid from revised TROPOMI to WRF-Chem grid, conservative method
+            # Refs: https://xesmf.readthedocs.io/en/latest/notebooks/Pure_numpy.html?highlight=conservative#Regridding
+            lon_wrf_value = lon_wrf.values
+            lat_wrf_value = lat_wrf.values
+            grid_in={'lon': lon, 'lat': lat, 'lon_b': lon_trp_b, 'lat_b': lat_trp_b }
+            grid_out ={'lon': lon_wrf_value, 'lat': lat_wrf_value, 'lon_b': lon_wrf_b, 'lat_b': lat_wrf_b}
+            regridder = xe.Regridder(grid_in, grid_out, 'conservative', ignore_degenerate=True)
+            no2_trp_regrid = regridder(no2colrev)
+
+            ind = np.where(no2_trp_regrid <= 0.0e-30)
+            if (ind != []):
+                no2_trp_regrid[ind] = np.nan
+
+            no2colomi_avg[:,:,trk] = no2_trp_regrid[:,:]
+            print('regridded NO2 column', np.nanmin(no2_trp_regrid), np.nanmax(no2_trp_regrid))
+
+
+        #-- final averaged data ---
+        # averaged slant NO2 columns in standard product, WRF-Chem domain
+        slantcol2 = slantcol / no2numomi_avg
+
+        # WRF-Chem arrays for comparison
+        zmax_default = np.nanmax(zmax_model)
+        print('zmax_default', zmax_default, np.nanmin(zmax_model))
+        no2_wrf_forcmp[:, :] = np.nansum(wrfno2_omi_avg[0:zmax_default+1,:,:],axis=0)
+
+        no2_omi_forcmp = np.nanmean(no2colomi_avg, axis=2)
+        slantcol_forcmp = np.nanmean(slantcol2, axis=2)        
+
+        print('OMI NO2 after AMF replacement:', np.nanmin(no2_omi_forcmp)/1e15, np.nanmax(no2_omi_forcmp)/1e15 )
+        print('WRF-Chem NO2:', np.nanmin(no2_wrf_forcmp)/1e15, np.nanmax(no2_wrf_forcmp)/1e15)
+         
+      
+       #----------------Output and Plotting-------------------
+       # Configured in OutputPlot_Config.py     
+
+        fnnc = Outdir+ 'no2_tropomi_wchamf_'+str(year)+'_'+str(month)+'_'+str(day)+'.nc'
+        ot.outputnc_2d(fnnc, no2_omi_forcmp, 'NO2', 'molec cm-2')
+
+        fnnc = Outdir+ 'no2_wrfchem_'+str(year)+'_'+str(month)+'_'+str(day)+'.nc'
+        ot.outputnc_2d(fnnc, no2_wrf_forcmp, 'NO2', 'molec cm-2')
+       
+        outdata = np.nanmean(amf_model, axis=2)
+        fnnc = Outdir + 'amf_model_'+str(year)+'_'+str(month)+'_'+str(day) + '.nc'
+        ot.outputnc_2d(fnnc, outdata, 'amf_model', '1.0') 
+
+        outdata = np.nanmean(tamf_omi, axis=2)
+        fnnc = Outdir + 'amf_omi_'+str(year)+'_'+str(month)+'_'+str(day)
+        ot.outputnc_2d(fnnc, outdata, 'amf_omi', '1.0')                
+      
+        outdata = np.nanmean(amf_total, axis=2)
+        fnnc = Outdir + 'totalamf_omi_'+str(year)+'_'+str(month)+'_'+str(day)+'.nc'
+        ot.outputnc_2d(fnnc, outdata, 'totalamf_omi', '1.0')
+
+        fnnc = Outdir+ 'no2_total_slantcol_'+str(year)+'_'+str(month)+'_'+str(day)+'.nc'
+        ot.outputnc_2d(fnnc,slantcol_forcmp , 'NO2', 'molec cm-2')
+      
+        fnnc = Outdir+ 'zmax_'+str(year)+'_'+str(month)+'_'+str(day)+'.nc'
+        ot.outputnc_2d(fnnc, zmax_model, 'zmax', '1')
+
+
+    def converunit(self, invalue, inunit, outunit):
+        if (inunit == 'mole m-2' and outunit == 'molec cm-2'):
+            #outvalue = invalue * 1.0*6.02e23/100.0/100.0
+            outvalue = invalue * 6.02214e+19 # provided by the TROPOMI
+        else:
+            print('no unit is changed', invalue)
+        return outvalue
+    
+    def calamfwrfchem_tm5(self, scatw, wrfpreslayer, wrfno2layer, tpreslev, trplayer):
+        from scipy import interpolate 
+        nume = 0.0
+        deno = 0.0
+        amf_wrfchem = np.nan
+        
+        f = interpolate.interp1d(np.log10(wrfpreslayer),wrfno2layer, fill_value="extrapolate")      
+        wrfno2_omi_avg_tm5 = f(np.log10(tpreslev[:]))
+                   
+        if type(trplayer) != np.int32:
+            amf_wrfchem = np.nan
+        else:
+            for l in range(trplayer+1): # add all tropospheric layers
+                if l == 0:
+                    deltapres = tpreslev[l]-tpreslev[l+1]
+                else:
+                    deltapres = tpreslev[l-1]-tpreslev[l]
+                nume += scatw[l] *wrfno2_omi_avg_tm5[l]*deltapres
+                deno += wrfno2_omi_avg_tm5[l]*deltapres
+                
+            amf_wrfchem = nume / deno
+        return amf_wrfchem
+ 
+    def calamfwrfchem(self, scatw, wrfpreslayer, wrfno2layer, tpreslev, trplayer, zwrftrop, wrfno2layer_molec):
+        from scipy import interpolate 
+        nume = 0.0
+        deno = 0.0
+        amf_wrfchem = np.nan
+        
+        tpreslev[0] = wrfpreslayer[0] # set the surface pressure to wrf one
+        f = interpolate.interp1d(np.log10(tpreslev),scatw, fill_value="extrapolate")# relationship between pressure to avk
+        wrfavk = f(np.log10(wrfpreslayer[:])) #wrf-chem averaging kernel
+        
+        # add all tropospheric layers in WRF
+        for l in range(zwrftrop+1):
+            if (np.isinf(wrfavk[l]) == True) | (wrfavk[l] <= 0.0):
+                nume += wrfavk[l+1]*wrfno2layer_molec[l]
+                deno += wrfno2layer_molec[l]
+                print('error of ak here', l, wrfavk[l], wrfavk[l+1], wrfno2layer_molec[l])
+            else:
+                nume += wrfavk[l]*wrfno2layer_molec[l]
+                deno += wrfno2layer_molec[l]
+                
+        amf_wrfchem = nume / deno
+        return amf_wrfchem
+   
+#---    
+class wrf_chem_process:
+    def __init__(self):
+        self.wrf_omi_avg = {}
+         
+    def extractwrfdata(self, year, month ,day):
+        # extract the NO2 column and Pressure from WRF-Chem
+        # average between 13:00 and 14:00 localtime
+
+        fm = file_management()
+        keywords = '{:02d}'.format(month) + '{:02d}'.format(day)
+        subdirlist = fm.subdirlist(Basedir_wrfoutput, keyword = keywords)
+        subdir = subdirlist[0]        
+
+        if len(subdirlist) > 1:
+            print('Warning: more than one directories for day of ' + keywords)
+            
+        infile_wrf_12 = os.path.join(subdir, 'wrfout_d01_'+str(year) + '-'+ '{:02d}'.format(month) + '-'+'{:02d}'.format(day)+'_'+'12:00:00')
+        infile_wrf_18 = os.path.join(subdir, 'wrfout_d01_'+str(year) + '-'+ '{:02d}'.format(month) + '-'+'{:02d}'.format(day)+'_'+'18:00:00')
+        wrfdata_perfile_12 = self.readwrfoutput(infile_wrf_12)
+        wrfdata_perfile_18 = self.readwrfoutput(infile_wrf_18)
+
+        no2_perfile_12 = wrfdata_perfile_12['no2']
+        no2_perfile_18 = wrfdata_perfile_18['no2']
+
+        pres_perfile_12 = wrfdata_perfile_12['pb2']
+        pres_perfile_18 = wrfdata_perfile_18['pb2']
+
+        ph_perfile_12 = wrfdata_perfile_12['ph']
+        ph_perfile_18 = wrfdata_perfile_18['ph']
+
+        phb_perfile_12 = wrfdata_perfile_12['phb']
+        phb_perfile_18 = wrfdata_perfile_18['phb']
+
+        t_perfile_12 = wrfdata_perfile_12['t2']
+        t_perfile_18 = wrfdata_perfile_18['t2']
+        
+        layers = np.shape(no2_perfile_12)[1]
+        
+        wrf_omi_all_no2 = np.zeros([layers,xy[0],xy[1],2], dtype = np.float)
+        wrf_omi_ppm_no2 = np.zeros([layers,xy[0],xy[1],2], dtype = np.float)
+        wrf_omi_all_pres = np.zeros([layers,xy[0],xy[1],2], dtype = np.float)
+        wrf_omi_all_no2[:,:,:,:] = np.nan
+        wrf_omi_all_pres[:,:,:,:] = np.nan
+
+        no2_u2  = np.zeros([layers,xy[0],xy[1],2], dtype = np.float)
+        #pres_u2 = np.zeros([6, layers,xy[0],xy[1],2], dtype = np.float)
+        ph_u2   = np.zeros([layers+1,xy[0],xy[1],2], dtype = np.float)
+        phb_u2  = np.zeros([layers+1,xy[0],xy[1],2], dtype = np.float)
+        t_u2  = np.zeros([layers,xy[0],xy[1],2], dtype = np.float)
+        
+        no2_u2[:,:,:,:] = np.nan
+        #pres_u2[:,:,:,:] = np.nan
+        ph_u2[:,:,:,:] = np.nan
+        phb_u2[:,:,:,:] = np.nan
+        t_u2[:,:,:,:] = np.nan
+
+        for x in range(xy[0]):
+            for y in range(xy[1]):
+                # Get UTC time at local time 13:00 and 14:00 for each wrf grid
+                lat = wrfcoord['lat'][x,y]
+                lon = wrfcoord['lon'][x,y]
+                utc_13 = 13 - self.utchourfromlatlon(lat, lon, year, month, day) # Local - UTC
+                utc_14 = utc_13 + 1
+
+                # read the corresponding file, 12 or 18
+                if utc_13 >= 12.0 and utc_13 < 18.0:
+                    hour_u13 = int(utc_13-12)
+
+                    no2_u2[:,x,y,0]  = no2_perfile_12[hour_u13,:,x,y]
+                    wrf_omi_all_pres[:,x,y,0] = pres_perfile_12[hour_u13,:,x,y]
+
+                    ph_u2[:,x,y,0]   = ph_perfile_12[hour_u13,:,x,y]
+                    phb_u2[:,x,y,0]  = phb_perfile_12[hour_u13,:,x,y]
+                    t_u2[:,x,y,0]    = t_perfile_12[hour_u13,:,x,y]
+                    
+                    if utc_14 >= 12.0 and utc_14 < 18.0:
+                        hour_u14 = int(utc_14 - 12)
+                        no2_u2[:,x,y,1] = no2_perfile_12[hour_u14,:,x,y]
+                        wrf_omi_all_pres[:,x,y,1] = pres_perfile_12[hour_u14,:,x,y]
+
+                        ph_u2[:,x,y,1]  = ph_perfile_12[hour_u14,:,x,y]
+                        phb_u2[:,x,y,1] = phb_perfile_12[hour_u14,:,x,y]
+                        t_u2[:,x,y,1] = t_perfile_12[hour_u14,:,x,y]
+
+                    else:
+                        hour_u14 = int(utc_14 - 18)
+                        no2_u2[:,x,y,1]  = no2_perfile_18[hour_u14,:,x,y]
+                        wrf_omi_all_pres[:,x,y,1] = pres_perfile_18[hour_u14,:,x,y]
+
+                        ph_u2[:,x,y,1]  = ph_perfile_18[hour_u14,:,x,y]
+                        phb_u2[:,x,y,1] = phb_perfile_18[hour_u14,:,x,y]
+
+                        t_u2[:,x,y,1] = t_perfile_18[hour_u14,:,x,y]
+                else:
+                    hour_u13 = int(utc_13-18)
+                    no2_u2[:,x,y,0] = no2_perfile_18[hour_u13,:,x,y]
+                    wrf_omi_all_pres[:,x,y,0] = pres_perfile_18[hour_u13,:,x,y]
+
+                    ph_u2[:,x,y,0] = ph_perfile_18[hour_u13,:,x,y]
+                    phb_u2[:,x,y,0] = phb_perfile_18[hour_u13,:,x,y]
+
+                    t_u2[:,x,y,0] = t_perfile_18[hour_u13,:,x,y]
+                    
+                    hour_u14 = int(utc_14-18)
+                    no2_u2[:,x,y,1]  = no2_perfile_18[hour_u14,:,x,y]
+                    wrf_omi_all_pres[:,x,y,1] = pres_perfile_18[hour_u14,:,x,y]
+
+                    ph_u2[:,x,y,1] = ph_perfile_18[hour_u14,:,x,y]
+                    phb_u2[:,x,y,1] = phb_perfile_18[hour_u14,:,x,y]
+
+                    t_u2[:,x,y,1] = t_perfile_18[hour_u14,:,x,y]
+
+        # calculatethe NO2 column, convert the unit
+        for l in range(layers):
+            ad = wrf_omi_all_pres[l,:,:,0]*(28.97e-3)/(8.314*t_u2[l,:,:,0])
+            #print('ad', np.nanmin(ad), np.nanmax(ad))
+            zh = ((ph_u2[l+1,:,:,0] + phb_u2[l+1,:,:,0]) - (ph_u2[l,:,:,0]+phb_u2[l,:,:,0]))/9.81
+            wrf_omi_all_no2[l,:,:,0] = no2_u2[l,:,:,0]*zh*6.022e23/(28.97e-3)*1e-10*ad[:,:]
+            wrf_omi_ppm_no2[l,:,:,0] = no2_u2[l,:,:,0]
+            ad2 = wrf_omi_all_pres[l,:,:,1]*(28.97e-3)/(8.314*t_u2[l,:,:,1])
+            zh2 = ((ph_u2[l+1,:,:,1] + phb_u2[l+1,:,:,1]) - (ph_u2[l,:,:,1]+phb_u2[l,:,:,1]))/9.81
+            wrf_omi_all_no2[l,:,:,1] = no2_u2[l,:,:,1]*zh2*6.022e23/(28.97e-3)*1e-10*ad2[:,:]
+            wrf_omi_ppm_no2[l,:,:,1] = no2_u2[l,:,:,1]
+
+               
+       # average the wrfno2_omi between 13:00 and 14:00 localtime
+        wrf_omi_avg_no2 = np.nanmean(wrf_omi_all_no2, axis=3)
+        wrf_omi_avg_pres = np.nanmean(wrf_omi_all_pres, axis=3)
+        wrf_omi_avg_no2_ppm = np.nanmean(wrf_omi_ppm_no2, axis=3)
+
+        self.wrf_omi_avg['no2'] = wrf_omi_avg_no2[:,:,:]
+        self.wrf_omi_avg['pres'] = wrf_omi_avg_pres[:,:,:]
+        self.wrf_omi_avg['no2ppm'] = wrf_omi_avg_no2_ppm[:,:,:] 
+
+        print('WRF-Chem:', np.nanmin(wrf_omi_avg_no2), np.nanmax(wrf_omi_avg_no2), np.nanmin(wrf_omi_avg_pres), np.nanmax(wrf_omi_avg_pres))
+      
+
+    def utchourfromlatlon(self,lat,lon,year, month, day):
+        # convert between UTC time and local time according to lon and lat
+
+        lon2 = lon.values.tolist()
+        lat2 = lat.values.tolist()
+        timezone_str = tf.timezone_at(lng=lon2, lat=lat2)
+        
+        if timezone_str == None:
+            if lon > -100.0:
+                timezone_str = 'America/New_York'
+            else:
+                timezone_str = 'America/Los_Angeles'
+        tz = pytz.timezone(timezone_str)
+        d = datetime(year,month, day, 00,00,00)
+        uos = tz.utcoffset(d, is_dst=True)
+        utchour = uos.seconds/60.0/60.0
+        utcday = uos.days
+        if utcday < 0:
+            utchour = (24-utchour)*-1 # Local - UTC
+        return utchour
+              
+
+    def readwrfoutput(self, infile):
+        # read wrf-chem output, return wrf-chem data directory
+
+        print('--> reading wrf output file of : ', infile   )
+        ncfile = Dataset(infile,'r',format = 'NETCDF4_CLASSIC')
+        wrfdata = {}
+        
+        no2data = wrf.getvar(ncfile, 'no2',timeidx=wrf.ALL_TIMES)  # NO2 Mixing ratio, ppmv
+        tdata = wrf.getvar(ncfile, 'T',timeidx=wrf.ALL_TIMES)      # K,perturbation potential temperature theta-t0
+        pdata = wrf.getvar(ncfile, 'P',timeidx=wrf.ALL_TIMES)      # Pa,perturbation pressure
+        pbdata = wrf.getvar(ncfile, 'PB',timeidx=wrf.ALL_TIMES)    # Pa,base state pressure
+        phdata = wrf.getvar(ncfile, 'PH',timeidx=wrf.ALL_TIMES)
+        phbdata = wrf.getvar(ncfile, 'PHB',timeidx=wrf.ALL_TIMES)
+        
+
+        # presure: base state + PB (KSMP)
+        pb2data = np.zeros([np.shape(pdata)[0], np.shape(pdata)[1], np.shape(pdata)[2], np.shape(pdata)[3]],dtype=np.float)
+        pb2data[:,:,:,:] = pdata[:,:,:,:]+ pbdata[:,:,:,:]
+
+        # convert the perturbation potential temperature (from 300K reference) to temp
+        tbdata = np.zeros([np.shape(tdata)[0], np.shape(tdata)[1], np.shape(tdata)[2], np.shape(tdata)[3]],dtype=np.float)
+        tbdata[:,:,:,:] =(300.0+tdata[:,:,:,:])*((pb2data[:,:,:,:]/1.0e5)**0.286)
+        
+        wrfdata = {'no2': no2data, 'pb2':pb2data, 'ph':phdata, 'phb': phbdata, 't2': tbdata}
+
+        ncfile.close()        
+        return wrfdata
+        
+#--- 
+class tropomi_process:
+    def __init__(self, year, month, day):
+        self.omidata_alltrack = []
+        self.scatwts_alltrack = []
+        self.year = year
+        self.month = month
+        self.day = day
+        
+    def avgtropomi(self):
+        year = self.year
+        month = self.month
+        day = self.day
+
+        fm = file_management()
+        timeindex = '{:04d}'.format(year)+'{:02d}'.format(month) + '{:02d}'.format(day)
+        subdirlist = fm.subdirlist(Basedir_tropomi)
+
+        omidata_alltrack = []
+        
+        for subdir in subdirlist:
+            fflist = fm.filelist(subdir, keyword='____'+timeindex)
+            tracknumber = len(fflist)
+            for infile in fflist:
+                # identify if the swath cover the WRF-Chem domain
+                llregion = self.identifyregion(infile)
+                if llregion == True:
+                    omidata = self.readtropomi(infile)
+                    omidata_alltrack.append(omidata)
+                else:
+                    pass
+                
+        self.omidata_alltrack = omidata_alltrack
+        
+    def identifyregion(self, infile):
+        # identify if the swath cover the WRF-Chem domain
+        # wrf-chem domain
+        wrflonlat = wrfcoord
+        wrflon = wrflonlat['lon']
+        wrflat = wrflonlat['lat']
+
+        # identify if the file is located in the region or not
+        ds = Dataset(infile,"r")
+        variable = np.squeeze(wrf.to_np(ds.groups['PRODUCT']['latitude'][:,:,:]))
+        lat_ind = variable
+        variable = np.squeeze(wrf.to_np(ds.groups['PRODUCT']['longitude'][:,:,:]))
+        lon_ind = variable
+        ds.close()
+
+        # determine the location in wrfchem
+        locind = extractwrfcoord(lats=lat_ind, lons=lon_ind)
+
+        wrf_ind_x = int(np.shape(wrflon)[0]) # lat
+        wrf_ind_y = int(np.shape(wrflon)[1]) # lon
+
+        llregion = False
+        xtomi = locind[0,:]
+        ytomi = locind[1,:]
+
+        ind = np.where((xtomi >= 0.0) & (ytomi >= 0.0) & (xtomi < wrf_ind_y) & (ytomi < wrf_ind_x))
+        if np.shape(ind)[1] == 0:
+            pass
+        else:
+           llregion = True
+        return llregion
+                    
+    def readtropomi(self,infile):
+        # read tropomi swath L2 NO2 data, return OMI NO2 data directory
+        omidata = {}
+        print('--> reading tropomi datafile of : ', infile)
+        ds = Dataset(infile,"r")
+        
+        variable = ds.groups['PRODUCT']['nitrogendioxide_tropospheric_column']
+        omidata['nitrogendioxide_tropospheric_column'] = variable
+        
+        variable = ds.groups['PRODUCT']['qa_value']
+        omidata['qa_value'] = variable        
+        
+        variable = ds.groups['PRODUCT']['averaging_kernel']
+        omidata['averaging_kernel'] = variable
+        
+        variable = ds.groups['PRODUCT']['air_mass_factor_total']
+        omidata['air_mass_factor_total'] = variable
+        
+        variable = ds.groups['PRODUCT']['air_mass_factor_troposphere']
+        omidata['air_mass_factor_troposphere'] = variable
+        
+        variable = ds.groups['PRODUCT']['latitude']
+        omidata['latitude'] = variable
+        
+        variable = ds.groups['PRODUCT']['longitude']
+        omidata['longitude'] = variable
+        
+        variable = ds.groups['PRODUCT']['tm5_constant_a']
+        omidata['tm5_constant_a'] = variable
+        
+        variable = ds.groups['PRODUCT']['tm5_constant_b']
+        omidata['tm5_constant_b'] = variable
+        
+        variable = ds.groups['PRODUCT']['tm5_tropopause_layer_index']
+        omidata['tm5_tropopause_layer_index'] = variable
+        
+        variable = ds.groups['PRODUCT']['SUPPORT_DATA']['INPUT_DATA']['surface_pressure']
+        omidata['surface_pressure'] = variable
+        
+        variable = ds.groups['PRODUCT']['SUPPORT_DATA']['INPUT_DATA']['cloud_fraction_crb']
+        omidata['cloud_fraction_crb'] = variable
+
+        variable = ds.groups['PRODUCT']['SUPPORT_DATA']['DETAILED_RESULTS']['nitrogendioxide_stratospheric_column']
+        omidata['nitrogendioxide_stratospheric_column'] = variable
+
+        variable = ds.groups['PRODUCT']['SUPPORT_DATA']['DETAILED_RESULTS']['air_mass_factor_stratosphere']
+        omidata['air_mass_factor_stratosphere'] = variable
+        
+        variable = ds.groups['PRODUCT']['SUPPORT_DATA']['DETAILED_RESULTS']['nitrogendioxide_slant_column_density']
+        omidata['nitrogendioxide_slant_column_density'] = variable
+       
+        variable = ds.groups['PRODUCT']['SUPPORT_DATA']['GEOLOCATIONS']['latitude_bounds']
+        omidata['latitude_bounds'] = variable #time x scanline x groud_pixel x corners, 1x3245x450x4
+
+        variable = ds.groups['PRODUCT']['SUPPORT_DATA']['GEOLOCATIONS']['longitude_bounds']
+        omidata['longitude_bounds'] = variable #time x scanline x groud_pixel x corners, 1x3245x450x4
+
+        # calculate the preslev
+        pleva = omidata['tm5_constant_a']
+        plevb = omidata['tm5_constant_b']
+        
+        spre = np.squeeze(omidata['surface_pressure'], axis=0)
+        aks = omidata['averaging_kernel']
+                
+        FillValue = omidata['surface_pressure']._FillValue
+        preslev = np.copy(aks)
+        preslev[:,:,:,:]  = np.nan
+        aks = None # to save memory
+        del aks
+        
+        spre[np.where(spre == FillValue)] = np.nan
+
+        for l in range(np.shape(preslev)[3]):
+            preslev[0,:,:,l] = (pleva[l,0]+spre[:,:]*plevb[l,0] + pleva[l,1]+spre[:,:]*plevb[l,1]) / 2.0 # center of the vertical layer            
+        omidata['preslev'] = preslev
+        
+        return omidata
+        ds.close()
+               
+#---
+
+
+if __name__ == "__main__":
+    # year, month, day
+    print(sys.argv[1], sys.argv[2], sys.argv[3])
+    main(int(sys.argv[1]), int(sys.argv[2]),int(sys.argv[3]))
+
diff --git a/to_generalize/run_daily_trp_no2.sh b/to_generalize/run_daily_trp_no2.sh
new file mode 100755
index 00000000..a9067a57
--- /dev/null
+++ b/to_generalize/run_daily_trp_no2.sh
@@ -0,0 +1,36 @@
+#!/bin/bash -l
+
+#SBATCH --job-name=mlitrop
+#SBATCH --partition=hera
+#SBATCH --time=08:00:00
+# -- Request 16 cores
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=1
+# -- Specify under which account a job should run
+#SBATCH --account=rcm2
+
+
+echo '=== Processing TROPOMI and WRF-Chem NO2 Columns ==='
+
+export Basedir_tropomi='/scratch1/BMC/rcm2/mli/tropomi_data/NO2_global/'
+export Baseoutdir='/scratch1/BMC/rcm2/mli/outdir_12km_noPM_baseline_bmc_cams/'
+export Basedir_wrfoutput='/scratch1/BMC/rcm2/mli/nyc18_cams/run_12km_five18_bmcdVCP_fog_wofire_BEIS_0.5ISO/Output/'
+export Geofile='/scratch1/BMC/rcm2/mli/nyc18_WPS/WPSV4.0/geo_em.d01.nc' # TO GET THE WRF boundaries
+
+echo "---> Data Locations < ---"
+echo "TROPOMI data are in:    " $Basedir_tropomi
+echo "WRF-Chem data are in:   " $Basedir_wrfoutput
+echo "Geophysical data are in:" $Geofile
+echo "Out data locations:     " $Baseoutdir
+
+year=2018
+month=6
+logdir=logs
+
+
+for day in $(seq 1 1 15) 
+do 
+    echo "Processing TROPOMI data in " $year $month $day
+    python CMP_WRFChem_TROPOMI_NO2Col_Daily_12km_Conservative_v3.py $year $month $day > $logdir/log_$year"_"$month"_"$day
+done
+