From 2a7b83e923c264f49a378c6963044ebcc6a83d67 Mon Sep 17 00:00:00 2001
From: mgrover1 <mgroverwx@gmail.com>
Date: Fri, 3 Nov 2023 11:04:18 -0500
Subject: [PATCH] ADD: Add new VAP section with SQUIRE

---
 VAPs/README.md                    |    7 +
 VAPs/squire/intro-to-squire.ipynb | 3810 +++++++++++++++++++++++++++++
 _toc.yml                          |    5 +
 3 files changed, 3822 insertions(+)
 create mode 100644 VAPs/README.md
 create mode 100644 VAPs/squire/intro-to-squire.ipynb

diff --git a/VAPs/README.md b/VAPs/README.md
new file mode 100644
index 00000000..87114bbb
--- /dev/null
+++ b/VAPs/README.md
@@ -0,0 +1,7 @@
+# Value Added Products
+
+[Value-added products (VAPs)](https://www.arm.gov/capabilities/science-data-products/vaps) are higher-order data products that have been analyzed and processed to ease scientists’ use of ARM data in atmospheric research and global climate models.
+
+VAPs are created by using existing ARM datastreams as input. Scientists work as translators analyzing the data in conjunction with research community needs, and then applying algorithms or models to enhance users’ scientific research and model development. VAPs provide an important translation between the instrumental measurements and the geophysical quantities needed for scientific analysis.
+
+Priorities for VAP development needs are solicited from the research community in cooperation with the translator team.
\ No newline at end of file
diff --git a/VAPs/squire/intro-to-squire.ipynb b/VAPs/squire/intro-to-squire.ipynb
new file mode 100644
index 00000000..f51423c0
--- /dev/null
+++ b/VAPs/squire/intro-to-squire.ipynb
@@ -0,0 +1,3810 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"../../images/arm_logo.png\" width=250 alt=\"ARM Logo\"></img>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Surface Qualatative Precipiation Estimate (SQUIRE) Value-added Product"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "This is a notebook focused on orienting users to one of ARM's Value-Added Products, SQUIRE! SQUIRE is a product developed to make data from scanning precipitation radars more useful - especially in complex terrain. One key site of opportunity is the Surface Atmosphere Integrated Field Laboratory, which was located near Crested Butte, Colorado. The radar was located in a valley between steep mountain peaks, making precipitation products from the instrument difficult to use.\n",
+    "\n",
+    "1. Introduction to SQUIRE\n",
+    "1. How to access the data and load using Xarray\n",
+    "1. Compare to weighted rain gauge data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "You will need to be familiar with the basics of Xarray, working with ARM data, and mapping in Python!\n",
+    "\n",
+    "| Concepts | Importance | Notes |\n",
+    "| --- | --- | --- |\n",
+    "| [Intro to Cartopy](https://foundations.projectpythia.org/core/cartopy/cartopy.html) | Necessary | |\n",
+    "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n",
+    "| [Intro to Xarray](https://foundations.projectpythia.org/core/xarray/xarray-intro.html) | Helpful | How to read in data and plot |\n",
+    "\n",
+    "- **Time to learn**: 30 minutes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.2.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import act\n",
+    "import cmweather\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview of SQUIRE\n",
+    "\n",
+    "SQUIRE builds off the Corrected Moments in Antenna Coordinates (CMAC)  product, which includes corrections related to beam blockage, de-aliased Doppler velocities, corrected reflectivity for liquid water path attenuation, differential phase corrected for non-uniform beam filling, and the integration of individual sweeps into volumes. For the precipitation estimation, empirical relationships of the equivalent radar reflectivity factor (Ze) to liquid-equivalent snowfall rates (Ze = aSb) or rainfall rates (Ze = aRb), are applied to the CMAC-corrected observations. The coefficients a and b vary with hydrometeor characteristics such as size distribution and density of snow crystals. Therefore, an ensemble approach with multiple a and b coefficients is used in order to better describe the spread within the precipitation estimates.\n",
+    "\n",
+    "As an evaluation VAP, it is requested that users of the data communicate closely with the VAP points of contact especially in communicating issues.\n",
+    "\n",
+    "This VAP is useful for those interested in gridded precipitation data. This is especially useful not only within meteorological applications but also hydrology. The earth system modeling community may use this to compare with liquid precipitation estimates from simulations.\n",
+    "\n",
+    "From Bukovčić et al. (2018) and Matrosov et al.(2009), the initial liquid-equivalent snowfall rates chosen for this product are:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "| Peer-Reviewed Relationship | Z(S) Relationship |\n",
+    "| --- | --- |\n",
+    "| Wolfe and Snider (2012) | $Z = 110S^2$ |\n",
+    "| WSR-88D High Plains | $Z = 130S^2$ |\n",
+    "| Braham (1990) 1 | $Z = 67S^{1.28}$ |\n",
+    "| Braham (1990) 2 | $Z = 136S^{1.3}$ |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Within SQUIRE, CMAC precipitation estimates are then gridded to a Cartesian grid using nearest neighbor interpolation, with 250-meter grid spacing (horizontal and vertical), with a spatial domain of 20 kilometers (x) x 20 kilometers (y) x 5 kilometers (z), all in units of distance from the radar. The lowest vertical level is calculated, and the data set is subset for this vertical level, with the fields valid at the lowest vertical level for each grid point."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How to Access SQUIRE Data from ARM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Use the ARM Live API to Download the Data, using ACT\n",
+    "\n",
+    "The Atmospheric Data Community Toolkit (ACT) has a helpful module to interface with the data server:\n",
+    "* [Download Data API](https://arm-doe.github.io/ACT/API/generated/act.discovery.download_data.html#act.discovery.download_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setup our Download Query\n",
+    "Before downloading our data, we need to make sure we have an ARM Data Account, and ARM Live token. Both of these can be found using this link:\n",
+    "- [ARM Live Signup](https://adc.arm.gov/armlive/livedata/home)\n",
+    "\n",
+    "Once you sign up, you will see your token. Copy and replace that where we have `arm_username` and `arm_password` below.\n",
+    "\n",
+    "We will also need the datastream name, which is `gucxprecipradarsquireS2.c1`, representing:\n",
+    "- the location (GUC), specifically the S2 location\n",
+    "    - This is the second supplementary site (S2) in Gunnison \n",
+    "- the instrument (xprecipradar)\n",
+    "    - This is the scanning precipitation X-band radar\n",
+    "- the product (squire)\n",
+    "    - This is the value-added product described in this notebook \n",
+    "- the data level (c1)\n",
+    "    - This is the corrected, quality-controlled data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[DOWNLOADING] gucxprecipradarsquireS2.c1.20220314.024239.nc\n",
+      "\n",
+      "If you use these data to prepare a publication, please cite:\n",
+      "\n",
+      "Grover, M., O’Brien, J., Collis, S., Jackson, R., Sherman, Z., Raut, B.,\n",
+      "Theisen, A., & Tuftedal, M. Surface QUantitatIve pRecipitation Estimation\n",
+      "(SQUIRE) (XPRECIPRADARSQUIRE). Atmospheric Radiation Measurement (ARM) User\n",
+      "Facility. https://doi.org/10.5439/1884979\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "arm_username = os.getenv(\"ARM_USERNAME\")\n",
+    "arm_password = os.getenv(\"ARM_PASSWORD\")\n",
+    "\n",
+    "datastream = \"gucxprecipradarsquireS2.c1\"\n",
+    "\n",
+    "start_date = \"2022-03-14T02:41:00\"\n",
+    "end_date = \"2022-03-14T02:44:00\"\n",
+    "\n",
+    "files = act.discovery.download_data(arm_username,\n",
+    "                                    arm_password,\n",
+    "                                    datastream,\n",
+    "                                    start_date,\n",
+    "                                    end_date)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read and plot the data using xarray"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
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+       "  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",
+       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-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",
+       ".xr-no-icon {\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: 1, y: 161, x: 161)\n",
+       "Coordinates:\n",
+       "  * time                    (time) datetime64[ns] 2022-03-14T02:42:39\n",
+       "    lat                     (y) float64 dask.array&lt;chunksize=(161,), meta=np.ndarray&gt;\n",
+       "  * y                       (y) float64 -2e+04 -1.975e+04 ... 1.975e+04 2e+04\n",
+       "    lon                     (x) float64 dask.array&lt;chunksize=(161,), meta=np.ndarray&gt;\n",
+       "  * x                       (x) float64 -2e+04 -1.975e+04 ... 1.975e+04 2e+04\n",
+       "    z                       (time, y, x) float64 dask.array&lt;chunksize=(1, 161, 161), meta=np.ndarray&gt;\n",
+       "Data variables:\n",
+       "    DBZ                     (time, y, x) float64 dask.array&lt;chunksize=(1, 161, 161), meta=np.ndarray&gt;\n",
+       "    corrected_reflectivity  (time, y, x) float64 dask.array&lt;chunksize=(1, 161, 161), meta=np.ndarray&gt;\n",
+       "    rain_rate_A             (time, y, x) float64 dask.array&lt;chunksize=(1, 161, 161), meta=np.ndarray&gt;\n",
+       "    snow_rate_ws88diw       (time, y, x) float64 dask.array&lt;chunksize=(1, 161, 161), meta=np.ndarray&gt;\n",
+       "    snow_rate_m2009_1       (time, y, x) float64 dask.array&lt;chunksize=(1, 161, 161), meta=np.ndarray&gt;\n",
+       "    snow_rate_m2009_2       (time, y, x) float64 dask.array&lt;chunksize=(1, 161, 161), meta=np.ndarray&gt;\n",
+       "    snow_rate_ws2012        (time, y, x) float64 dask.array&lt;chunksize=(1, 161, 161), meta=np.ndarray&gt;\n",
+       "    lowest_height           (time, y, x) float64 dask.array&lt;chunksize=(1, 161, 161), meta=np.ndarray&gt;\n",
+       "Attributes: (12/24)\n",
+       "    command_line:          python run_squire.py\n",
+       "    Conventions:           ARM-1.3 CF/Radial instrument_parameters\n",
+       "    process_version:       SQUIRE 1.0\n",
+       "    dod_version:           xprecipradarsquire-c1-1.0\n",
+       "    site_id:               guc\n",
+       "    platform_id:           xprecipradarsquire\n",
+       "    ...                    ...\n",
+       "    mentors:               https://www.arm.gov/connect-with-arm/organization/...\n",
+       "    source:                Colorado State University&#x27;s X-Band Precipitation R...\n",
+       "    input_datastreams:     gucxprecipradarcmacppiS2.c1\n",
+       "    fields:                DBZ, corrected_reflectivity, time, lowest_height, ...\n",
+       "    history:               created by mgrover4 on Cumulus at 2023-02-06 19:10...\n",
+       "    DOD:                   gucxprecipradarsquire.c1</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-8895e51b-1467-4b52-8915-68047522a10d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8895e51b-1467-4b52-8915-68047522a10d' 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>: 1</li><li><span class='xr-has-index'>y</span>: 161</li><li><span class='xr-has-index'>x</span>: 161</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e7dbe03d-add1-4a64-a836-ad57e7d95909' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e7dbe03d-add1-4a64-a836-ad57e7d95909' 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'>2022-03-14T02:42:39</div><input id='attrs-3516b39c-983a-4ebc-ac81-50bee981d5bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3516b39c-983a-4ebc-ac81-50bee981d5bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-940c12de-0123-40dd-a2ca-d8e14488519f' class='xr-var-data-in' type='checkbox'><label for='data-940c12de-0123-40dd-a2ca-d8e14488519f' 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;2022-03-14T02:42:39.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(161,), meta=np.ndarray&gt;</div><input id='attrs-65f9311d-9d51-49ec-9949-e52072ae4368' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65f9311d-9d51-49ec-9949-e52072ae4368' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e9371fc-5834-4962-bf1e-290a16021f9c' class='xr-var-data-in' type='checkbox'><label for='data-4e9371fc-5834-4962-bf1e-290a16021f9c' 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>long_name :</span></dt><dd>North latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>valid_min :</span></dt><dd>-90</dd><dt><span>valid_max :</span></dt><dd>90</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 1.26 kiB </td>\n",
+       "                        <td> 1.26 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (161,) </td>\n",
+       "                        <td> (161,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >161</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></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'>-2e+04 -1.975e+04 ... 2e+04</div><input id='attrs-321a6253-32aa-4f57-a264-6c16d52a73cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-321a6253-32aa-4f57-a264-6c16d52a73cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a811f4d2-c989-4c6a-9978-a2ddf231b5f7' class='xr-var-data-in' type='checkbox'><label for='data-a811f4d2-c989-4c6a-9978-a2ddf231b5f7' 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>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>long_name :</span></dt><dd>Y distance on the projection plane from the origin</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([-20000., -19750., -19500., -19250., -19000., -18750., -18500., -18250.,\n",
+       "       -18000., -17750., -17500., -17250., -17000., -16750., -16500., -16250.,\n",
+       "       -16000., -15750., -15500., -15250., -15000., -14750., -14500., -14250.,\n",
+       "       -14000., -13750., -13500., -13250., -13000., -12750., -12500., -12250.,\n",
+       "       -12000., -11750., -11500., -11250., -11000., -10750., -10500., -10250.,\n",
+       "       -10000.,  -9750.,  -9500.,  -9250.,  -9000.,  -8750.,  -8500.,  -8250.,\n",
+       "        -8000.,  -7750.,  -7500.,  -7250.,  -7000.,  -6750.,  -6500.,  -6250.,\n",
+       "        -6000.,  -5750.,  -5500.,  -5250.,  -5000.,  -4750.,  -4500.,  -4250.,\n",
+       "        -4000.,  -3750.,  -3500.,  -3250.,  -3000.,  -2750.,  -2500.,  -2250.,\n",
+       "        -2000.,  -1750.,  -1500.,  -1250.,  -1000.,   -750.,   -500.,   -250.,\n",
+       "            0.,    250.,    500.,    750.,   1000.,   1250.,   1500.,   1750.,\n",
+       "         2000.,   2250.,   2500.,   2750.,   3000.,   3250.,   3500.,   3750.,\n",
+       "         4000.,   4250.,   4500.,   4750.,   5000.,   5250.,   5500.,   5750.,\n",
+       "         6000.,   6250.,   6500.,   6750.,   7000.,   7250.,   7500.,   7750.,\n",
+       "         8000.,   8250.,   8500.,   8750.,   9000.,   9250.,   9500.,   9750.,\n",
+       "        10000.,  10250.,  10500.,  10750.,  11000.,  11250.,  11500.,  11750.,\n",
+       "        12000.,  12250.,  12500.,  12750.,  13000.,  13250.,  13500.,  13750.,\n",
+       "        14000.,  14250.,  14500.,  14750.,  15000.,  15250.,  15500.,  15750.,\n",
+       "        16000.,  16250.,  16500.,  16750.,  17000.,  17250.,  17500.,  17750.,\n",
+       "        18000.,  18250.,  18500.,  18750.,  19000.,  19250.,  19500.,  19750.,\n",
+       "        20000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(161,), meta=np.ndarray&gt;</div><input id='attrs-1f21a285-1848-4254-b2dc-66d98617371c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f21a285-1848-4254-b2dc-66d98617371c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f91ad5ae-13b2-4d6b-b6d6-b27a098c875f' class='xr-var-data-in' type='checkbox'><label for='data-f91ad5ae-13b2-4d6b-b6d6-b27a098c875f' 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>long_name :</span></dt><dd>East longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>valid_min :</span></dt><dd>-180</dd><dt><span>valid_max :</span></dt><dd>180</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 1.26 kiB </td>\n",
+       "                        <td> 1.26 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (161,) </td>\n",
+       "                        <td> (161,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >161</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></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'>-2e+04 -1.975e+04 ... 2e+04</div><input id='attrs-84807bef-8d8c-41cd-b16f-ad56087cfcef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84807bef-8d8c-41cd-b16f-ad56087cfcef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6fe2d97-8340-4271-aaa6-48648cee897d' class='xr-var-data-in' type='checkbox'><label for='data-f6fe2d97-8340-4271-aaa6-48648cee897d' 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>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>long_name :</span></dt><dd>X distance on the projection plane from the origin</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([-20000., -19750., -19500., -19250., -19000., -18750., -18500., -18250.,\n",
+       "       -18000., -17750., -17500., -17250., -17000., -16750., -16500., -16250.,\n",
+       "       -16000., -15750., -15500., -15250., -15000., -14750., -14500., -14250.,\n",
+       "       -14000., -13750., -13500., -13250., -13000., -12750., -12500., -12250.,\n",
+       "       -12000., -11750., -11500., -11250., -11000., -10750., -10500., -10250.,\n",
+       "       -10000.,  -9750.,  -9500.,  -9250.,  -9000.,  -8750.,  -8500.,  -8250.,\n",
+       "        -8000.,  -7750.,  -7500.,  -7250.,  -7000.,  -6750.,  -6500.,  -6250.,\n",
+       "        -6000.,  -5750.,  -5500.,  -5250.,  -5000.,  -4750.,  -4500.,  -4250.,\n",
+       "        -4000.,  -3750.,  -3500.,  -3250.,  -3000.,  -2750.,  -2500.,  -2250.,\n",
+       "        -2000.,  -1750.,  -1500.,  -1250.,  -1000.,   -750.,   -500.,   -250.,\n",
+       "            0.,    250.,    500.,    750.,   1000.,   1250.,   1500.,   1750.,\n",
+       "         2000.,   2250.,   2500.,   2750.,   3000.,   3250.,   3500.,   3750.,\n",
+       "         4000.,   4250.,   4500.,   4750.,   5000.,   5250.,   5500.,   5750.,\n",
+       "         6000.,   6250.,   6500.,   6750.,   7000.,   7250.,   7500.,   7750.,\n",
+       "         8000.,   8250.,   8500.,   8750.,   9000.,   9250.,   9500.,   9750.,\n",
+       "        10000.,  10250.,  10500.,  10750.,  11000.,  11250.,  11500.,  11750.,\n",
+       "        12000.,  12250.,  12500.,  12750.,  13000.,  13250.,  13500.,  13750.,\n",
+       "        14000.,  14250.,  14500.,  14750.,  15000.,  15250.,  15500.,  15750.,\n",
+       "        16000.,  16250.,  16500.,  16750.,  17000.,  17250.,  17500.,  17750.,\n",
+       "        18000.,  18250.,  18500.,  18750.,  19000.,  19250.,  19500.,  19750.,\n",
+       "        20000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 161, 161), meta=np.ndarray&gt;</div><input id='attrs-78907f0f-50fd-4e1a-8374-c7e484224ec0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78907f0f-50fd-4e1a-8374-c7e484224ec0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57a63b38-225f-41f3-8fc2-9e7c95464d38' class='xr-var-data-in' type='checkbox'><label for='data-57a63b38-225f-41f3-8fc2-9e7c95464d38' 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>long_name :</span></dt><dd>Z distance on the projection plane from the origin</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_z_coordinate</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 202.51 kiB </td>\n",
+       "                        <td> 202.51 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 161, 161) </td>\n",
+       "                        <td> (1, 161, 161) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a40c0980-2ed7-4305-8061-b8369b108683' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a40c0980-2ed7-4305-8061-b8369b108683' class='xr-section-summary' >Data variables: <span>(8)</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>DBZ</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 161, 161), meta=np.ndarray&gt;</div><input id='attrs-9e6fd1d7-ef23-49c7-afcd-ff6139cc6c2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e6fd1d7-ef23-49c7-afcd-ff6139cc6c2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-072fa7ea-b813-49b3-a455-30a83831289b' class='xr-var-data-in' type='checkbox'><label for='data-072fa7ea-b813-49b3-a455-30a83831289b' 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>long_name :</span></dt><dd>Equivalent Radar Reflectivity Factor</dd><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>standard_name :</span></dt><dd>equivalent_reflectivity_factor</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 202.51 kiB </td>\n",
+       "                        <td> 202.51 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 161, 161) </td>\n",
+       "                        <td> (1, 161, 161) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"194\" height=\"184\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
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+       "  <text x=\"7.474299\" y=\"147.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,147.474299)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_reflectivity</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 161, 161), meta=np.ndarray&gt;</div><input id='attrs-2c0e5273-c98f-4da6-a92b-765355dec8ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c0e5273-c98f-4da6-a92b-765355dec8ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1cbbe247-1240-4965-ba88-6b8b61f8d664' class='xr-var-data-in' type='checkbox'><label for='data-1cbbe247-1240-4965-ba88-6b8b61f8d664' 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>long_name :</span></dt><dd>Corrected reflectivity</dd><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>standard_name :</span></dt><dd>corrected_equivalent_reflectivity_factor</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 202.51 kiB </td>\n",
+       "                        <td> 202.51 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 161, 161) </td>\n",
+       "                        <td> (1, 161, 161) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"194\" height=\"184\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "\n",
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+       "\n",
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+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"84.948598\" y=\"154.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >161</text>\n",
+       "  <text x=\"164.948598\" y=\"74.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,74.948598)\">161</text>\n",
+       "  <text x=\"7.474299\" y=\"147.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,147.474299)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_rate_A</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 161, 161), meta=np.ndarray&gt;</div><input id='attrs-7a49c336-1f5a-4b04-8602-7e2e08b297a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a49c336-1f5a-4b04-8602-7e2e08b297a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-568b97e8-699e-4735-83c2-27b6a45a2207' class='xr-var-data-in' type='checkbox'><label for='data-568b97e8-699e-4735-83c2-27b6a45a2207' 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>long_name :</span></dt><dd>Rainfall Rate from Specific Attenuation</dd><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>comment :</span></dt><dd>Rain rate calculated from specific_attenuation, R=43.5*specific_attenuation**0.79, note R=0.0 where norm coherent power &lt; 0.4 or rhohv &lt; 0.8</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 202.51 kiB </td>\n",
+       "                        <td> 202.51 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 161, 161) </td>\n",
+       "                        <td> (1, 161, 161) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_rate_ws88diw</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 161, 161), meta=np.ndarray&gt;</div><input id='attrs-e824ec18-8f32-4b6d-9c4c-2a0337a28f49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e824ec18-8f32-4b6d-9c4c-2a0337a28f49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38c318f0-6fc2-410d-b39e-36df31d601c1' class='xr-var-data-in' type='checkbox'><label for='data-38c318f0-6fc2-410d-b39e-36df31d601c1' 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>long_name :</span></dt><dd>Snowfall rate from Z using WSR 88D High Plains</dd><dt><span>units :</span></dt><dd>mm/h</dd><dt><span>standard_name :</span></dt><dd>snowfall_rate</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>500</dd><dt><span>swe_ratio :</span></dt><dd>13.699</dd><dt><span>A :</span></dt><dd>40</dd><dt><span>B :</span></dt><dd>2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 202.51 kiB </td>\n",
+       "                        <td> 202.51 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 161, 161) </td>\n",
+       "                        <td> (1, 161, 161) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_rate_m2009_1</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 161, 161), meta=np.ndarray&gt;</div><input id='attrs-d22f9421-4aa3-4701-942d-0355def7e8b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d22f9421-4aa3-4701-942d-0355def7e8b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a725e890-f130-4eb4-8759-5c944b033b15' class='xr-var-data-in' type='checkbox'><label for='data-a725e890-f130-4eb4-8759-5c944b033b15' 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>long_name :</span></dt><dd>Snowfall rate from Z using Matrosov et al.(2009) Braham(1990) 1</dd><dt><span>units :</span></dt><dd>mm/h</dd><dt><span>standard_name :</span></dt><dd>snowfall_rate</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>500</dd><dt><span>swe_ratio :</span></dt><dd>13.699</dd><dt><span>A :</span></dt><dd>67</dd><dt><span>B :</span></dt><dd>1.28</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 202.51 kiB </td>\n",
+       "                        <td> 202.51 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 161, 161) </td>\n",
+       "                        <td> (1, 161, 161) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 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>snow_rate_m2009_2</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 161, 161), meta=np.ndarray&gt;</div><input id='attrs-527b84a1-8660-4579-a747-d77f62aa29e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-527b84a1-8660-4579-a747-d77f62aa29e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23a34ab2-a629-4a6f-837f-4ee3d06f7fe6' class='xr-var-data-in' type='checkbox'><label for='data-23a34ab2-a629-4a6f-837f-4ee3d06f7fe6' 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>long_name :</span></dt><dd>Snowfall rate from Z using Matrosov et al.(2009) Braham(1990) 2</dd><dt><span>units :</span></dt><dd>mm/h</dd><dt><span>standard_name :</span></dt><dd>snowfall_rate</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>500</dd><dt><span>swe_ratio :</span></dt><dd>13.699</dd><dt><span>A :</span></dt><dd>114</dd><dt><span>B :</span></dt><dd>1.39</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 202.51 kiB </td>\n",
+       "                        <td> 202.51 kiB </td>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 161, 161) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_rate_ws2012</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 161, 161), meta=np.ndarray&gt;</div><input id='attrs-cd91e84e-ad85-40f0-8ad6-96a9aa9324cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd91e84e-ad85-40f0-8ad6-96a9aa9324cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ae0f05e-849c-4548-aaff-9b21aec7a1f5' class='xr-var-data-in' type='checkbox'><label for='data-6ae0f05e-849c-4548-aaff-9b21aec7a1f5' 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>long_name :</span></dt><dd>Snowfall rate from Z using Wolf and Snider (2012)</dd><dt><span>units :</span></dt><dd>mm/h</dd><dt><span>standard_name :</span></dt><dd>snowfall_rate</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>500</dd><dt><span>swe_ratio :</span></dt><dd>13.699</dd><dt><span>A :</span></dt><dd>110</dd><dt><span>B :</span></dt><dd>2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 202.51 kiB </td>\n",
+       "                        <td> 202.51 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 161, 161) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 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>lowest_height</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 161, 161), meta=np.ndarray&gt;</div><input id='attrs-37ef0cc5-c58d-4203-9d69-57f60af53441' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37ef0cc5-c58d-4203-9d69-57f60af53441' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94428012-6bae-4435-ab9a-12b31bda1861' class='xr-var-data-in' type='checkbox'><label for='data-94428012-6bae-4435-ab9a-12b31bda1861' 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>long_name :</span></dt><dd>Height of the lowest Radar Gate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-02938844-9333-4618-9cbd-ab6c3c7c24cc' class='xr-section-summary-in' type='checkbox'  ><label for='section-02938844-9333-4618-9cbd-ab6c3c7c24cc' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-099d89bb-26ab-4fdf-aee6-5208d4476db7' class='xr-index-data-in' type='checkbox'/><label for='index-099d89bb-26ab-4fdf-aee6-5208d4476db7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-03-14 02:42:39&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-be645278-0602-4ffe-85ff-682b8153179d' class='xr-index-data-in' type='checkbox'/><label for='index-be645278-0602-4ffe-85ff-682b8153179d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-20000.0, -19750.0, -19500.0, -19250.0, -19000.0, -18750.0, -18500.0,\n",
+       "       -18250.0, -18000.0, -17750.0,\n",
+       "       ...\n",
+       "        17750.0,  18000.0,  18250.0,  18500.0,  18750.0,  19000.0,  19250.0,\n",
+       "        19500.0,  19750.0,  20000.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=161))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-14f4f7e5-9b78-4270-9101-2a7effa72e57' class='xr-index-data-in' type='checkbox'/><label for='index-14f4f7e5-9b78-4270-9101-2a7effa72e57' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-20000.0, -19750.0, -19500.0, -19250.0, -19000.0, -18750.0, -18500.0,\n",
+       "       -18250.0, -18000.0, -17750.0,\n",
+       "       ...\n",
+       "        17750.0,  18000.0,  18250.0,  18500.0,  18750.0,  19000.0,  19250.0,\n",
+       "        19500.0,  19750.0,  20000.0],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=161))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1d3b329b-3491-4f5a-ae0d-bbbfd4da8610' class='xr-section-summary-in' type='checkbox'  ><label for='section-1d3b329b-3491-4f5a-ae0d-bbbfd4da8610' class='xr-section-summary' >Attributes: <span>(24)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>command_line :</span></dt><dd>python run_squire.py</dd><dt><span>Conventions :</span></dt><dd>ARM-1.3 CF/Radial instrument_parameters</dd><dt><span>process_version :</span></dt><dd>SQUIRE 1.0</dd><dt><span>dod_version :</span></dt><dd>xprecipradarsquire-c1-1.0</dd><dt><span>site_id :</span></dt><dd>guc</dd><dt><span>platform_id :</span></dt><dd>xprecipradarsquire</dd><dt><span>facility_id :</span></dt><dd>S2</dd><dt><span>data_level :</span></dt><dd>c1</dd><dt><span>location_description :</span></dt><dd>Gunnison, CO; Surface Atmosphere Integrated field Laboratory (GUC)</dd><dt><span>datastream :</span></dt><dd>gucxprecipradarsquireS2.c1</dd><dt><span>institution :</span></dt><dd>U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Climate Research Facility</dd><dt><span>references :</span></dt><dd>See XPRECIPRADAR Instrument Handbook</dd><dt><span>doi :</span></dt><dd>10.5439/1884979</dd><dt><span>comment :</span></dt><dd>This is highly experimental and initial data. There are many known and unknown issues. Please do not use before contacting the Translator responsible scollis@anl.gov</dd><dt><span>attributions :</span></dt><dd>This data is collected by the ARM Climate Research facility. Radar system is operated by the radar engineering team radar@arm.gov and the data is processed by the precipitation radar products team. LP code courtesy of Scott Giangrande BNL.</dd><dt><span>known_issues :</span></dt><dd>False phidp jumps in insect regions. Still uses old Giangrande code. Issues with some snow below melting layer.</dd><dt><span>developers :</span></dt><dd>Maxwell Grover, ANL. Joseph O&#x27;Brien, ANL. Robert Jackson, ANL. Zachary Sherman, ANL.</dd><dt><span>translator :</span></dt><dd>https://www.arm.gov/capabilities/instruments/xprecipradar</dd><dt><span>mentors :</span></dt><dd>https://www.arm.gov/connect-with-arm/organization/instrument-mentors/list#xprecipradar</dd><dt><span>source :</span></dt><dd>Colorado State University&#x27;s X-Band Precipitation Radar (XPRECIPRADAR) (DOI: 10.5439/1844501)</dd><dt><span>input_datastreams :</span></dt><dd>gucxprecipradarcmacppiS2.c1</dd><dt><span>fields :</span></dt><dd>DBZ, corrected_reflectivity, time, lowest_height, rain_rate_A, snow_rate_ws88diw, snow_rate_m2009_1, snow_rate_m2009_2, snow_rate_ws2012, z, y, x, lat, lon</dd><dt><span>history :</span></dt><dd>created by mgrover4 on Cumulus at 2023-02-06 19:10:49.47 using Py-ART</dd><dt><span>DOD :</span></dt><dd>gucxprecipradarsquire.c1</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:                 (time: 1, y: 161, x: 161)\n",
+       "Coordinates:\n",
+       "  * time                    (time) datetime64[ns] 2022-03-14T02:42:39\n",
+       "    lat                     (y) float64 dask.array<chunksize=(161,), meta=np.ndarray>\n",
+       "  * y                       (y) float64 -2e+04 -1.975e+04 ... 1.975e+04 2e+04\n",
+       "    lon                     (x) float64 dask.array<chunksize=(161,), meta=np.ndarray>\n",
+       "  * x                       (x) float64 -2e+04 -1.975e+04 ... 1.975e+04 2e+04\n",
+       "    z                       (time, y, x) float64 dask.array<chunksize=(1, 161, 161), meta=np.ndarray>\n",
+       "Data variables:\n",
+       "    DBZ                     (time, y, x) float64 dask.array<chunksize=(1, 161, 161), meta=np.ndarray>\n",
+       "    corrected_reflectivity  (time, y, x) float64 dask.array<chunksize=(1, 161, 161), meta=np.ndarray>\n",
+       "    rain_rate_A             (time, y, x) float64 dask.array<chunksize=(1, 161, 161), meta=np.ndarray>\n",
+       "    snow_rate_ws88diw       (time, y, x) float64 dask.array<chunksize=(1, 161, 161), meta=np.ndarray>\n",
+       "    snow_rate_m2009_1       (time, y, x) float64 dask.array<chunksize=(1, 161, 161), meta=np.ndarray>\n",
+       "    snow_rate_m2009_2       (time, y, x) float64 dask.array<chunksize=(1, 161, 161), meta=np.ndarray>\n",
+       "    snow_rate_ws2012        (time, y, x) float64 dask.array<chunksize=(1, 161, 161), meta=np.ndarray>\n",
+       "    lowest_height           (time, y, x) float64 dask.array<chunksize=(1, 161, 161), meta=np.ndarray>\n",
+       "Attributes: (12/24)\n",
+       "    command_line:          python run_squire.py\n",
+       "    Conventions:           ARM-1.3 CF/Radial instrument_parameters\n",
+       "    process_version:       SQUIRE 1.0\n",
+       "    dod_version:           xprecipradarsquire-c1-1.0\n",
+       "    site_id:               guc\n",
+       "    platform_id:           xprecipradarsquire\n",
+       "    ...                    ...\n",
+       "    mentors:               https://www.arm.gov/connect-with-arm/organization/...\n",
+       "    source:                Colorado State University's X-Band Precipitation R...\n",
+       "    input_datastreams:     gucxprecipradarcmacppiS2.c1\n",
+       "    fields:                DBZ, corrected_reflectivity, time, lowest_height, ...\n",
+       "    history:               created by mgrover4 on Cumulus at 2023-02-06 19:10...\n",
+       "    DOD:                   gucxprecipradarsquire.c1"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "squire_ds = xr.open_mfdataset(files)\n",
+    "squire_ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we read in the dataset, we see we have a few different snow rate variables:\n",
+    "| Variable Name | Peer-Reviewed Relationship | Z(S) Relationship |\n",
+    "| --- | --- | --- |\n",
+    "| **snow_rate_ws2012** | Wolfe and Snider (2012) | $$Z = 110S^2$$ |\n",
+    "| **snow_rate_ws88diw** | WSR-88D High Plains | $$Z = 130S^2$$ |\n",
+    "| **snow_rate_m2009_1** |Braham (1990) 1 | $$Z = 67S^{1.28}$$ |\n",
+    "| **snow_rate_m2009_2** | Braham (1990) 2 | $$Z = 136S^{1.3}$$ |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot the Wolf and Snider (2012) retrieval to start!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "squire_ds.snow_rate_ws2012.plot(cmap='ChaseSpectral');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, the axes are the x/y distance from the radar. Latitude and longitude information would be more helpful here!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "squire_ds.snow_rate_ws2012.plot(x='lon',\n",
+    "                                y='lat',\n",
+    "                                cmap='ChaseSpectral');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compare with Weighted Rain Gauge Data\n",
+    "We are interested in a weighted rain bucket gauge located near the main site (M1), with the specific instrument being the pluvio. We use the following block of code to access the `.a1` data, which has not been fully corrected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[DOWNLOADING] gucwbpluvio2M1.a1.20220314.000000.nc\n",
+      "\n",
+      "If you use these data to prepare a publication, please cite:\n",
+      "\n",
+      "Wang, D., Jane, M., Cromwell, E., Sturm, M., Irving, K., Delamere, J., &\n",
+      "Mockaitis, M. Weighing Bucket Precipitation Gauge (WBPLUVIO2). Atmospheric\n",
+      "Radiation Measurement (ARM) User Facility. https://doi.org/10.5439/1338194\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['/Users/mgrover/git_repos/ARM-Notebooks/VAPs/squire/gucwbpluvio2M1.a1/gucwbpluvio2M1.a1.20220314.000000.nc']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arm_username = os.getenv(\"ARM_USERNAME\")\n",
+    "arm_password = os.getenv(\"ARM_PASSWORD\")\n",
+    "\n",
+    "datastream = \"gucwbpluvio2M1.a1\"\n",
+    "\n",
+    "start_date = \"2022-03-14\"\n",
+    "end_date = \"2022-03-14\"\n",
+    "\n",
+    "files = act.discovery.download_data(arm_username,\n",
+    "                                    arm_password,\n",
+    "                                    datastream,\n",
+    "                                    start_date,\n",
+    "                                    end_date)\n",
+    "files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After we download the file, we can load it into our notebook using the ACT library, leveraging xarray under the hood."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mgrover/mambaforge/envs/arm-tutorial-dev/lib/python3.11/site-packages/act/io/armfiles.py:204: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.\n",
+      "  temp_ds = xr.Dataset(\n",
+      "/Users/mgrover/mambaforge/envs/arm-tutorial-dev/lib/python3.11/site-packages/act/io/armfiles.py:204: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.\n",
+      "  temp_ds = xr.Dataset(\n",
+      "/Users/mgrover/mambaforge/envs/arm-tutorial-dev/lib/python3.11/site-packages/act/io/armfiles.py:218: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.\n",
+      "  ds['base_time'].values = ds['base_time'].values.astype(desired_time_precision)\n",
+      "/Users/mgrover/mambaforge/envs/arm-tutorial-dev/lib/python3.11/site-packages/act/io/armfiles.py:219: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.\n",
+      "  ds['base_time'] = ds['base_time'].astype(desired_time_precision)\n"
+     ]
+    },
+    {
+     "data": {
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+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  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",
+       ".xr-index-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",
+       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-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",
+       ".xr-no-icon {\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: 1440)\n",
+       "Coordinates:\n",
+       "  * time              (time) datetime64[ns] 2022-03-14 ... 2022-03-14T23:59:00\n",
+       "Data variables: (12/22)\n",
+       "    base_time         datetime64[ns] 2022-03-14\n",
+       "    time_offset       (time) datetime64[ns] 2022-03-14 ... 2022-03-14T23:59:00\n",
+       "    intensity_rt      (time) float32 dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;\n",
+       "    accum_rtnrt       (time) float32 dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;\n",
+       "    accum_nrt         (time) float32 dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;\n",
+       "    accum_total_nrt   (time) float32 dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;\n",
+       "    ...                ...\n",
+       "    volt_min          (time) float32 dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;\n",
+       "    ptemp             (time) float32 dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;\n",
+       "    intensity_rtnrt   (time) float32 dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;\n",
+       "    lat               float32 ...\n",
+       "    lon               float32 ...\n",
+       "    alt               float32 ...\n",
+       "Attributes: (12/19)\n",
+       "    command_line:          wbpluvio2_ingest -s guc -f M1\n",
+       "    Conventions:           ARM-1.2\n",
+       "    process_version:       ingest-wbpluvio2-1.1-0.el7\n",
+       "    dod_version:           wbpluvio2-a1-1.0\n",
+       "    input_source:          /data/collection/guc/gucwbpluvio2M1.00/DL2.wbpluvi...\n",
+       "    site_id:               guc\n",
+       "    ...                    ...\n",
+       "    doi:                   10.5439/1338194\n",
+       "    history:               created by user dsmgr on machine flint at 2022-03-...\n",
+       "    _file_dates:           [&#x27;20220314&#x27;]\n",
+       "    _file_times:           [&#x27;000000&#x27;]\n",
+       "    _datastream:           gucwbpluvio2M1.a1\n",
+       "    _arm_standards_flag:   1</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-8e9f17e0-deb1-44b9-a8ec-bf92e57f3340' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8e9f17e0-deb1-44b9-a8ec-bf92e57f3340' 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>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-be7863df-2d01-43ea-83b2-ab5025d31389' class='xr-section-summary-in' type='checkbox'  checked><label for='section-be7863df-2d01-43ea-83b2-ab5025d31389' class='xr-section-summary' >Coordinates: <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 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'>2022-03-14 ... 2022-03-14T23:59:00</div><input id='attrs-ba50dc36-bd9a-4563-9f69-82e7a80b37f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba50dc36-bd9a-4563-9f69-82e7a80b37f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b86fa53-a518-4ecb-a18c-e6becd5f05fc' class='xr-var-data-in' type='checkbox'><label for='data-1b86fa53-a518-4ecb-a18c-e6becd5f05fc' 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>long_name :</span></dt><dd>Time offset from midnight</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-03-14T00:00:00.000000000&#x27;, &#x27;2022-03-14T00:01:00.000000000&#x27;,\n",
+       "       &#x27;2022-03-14T00:02:00.000000000&#x27;, ..., &#x27;2022-03-14T23:57:00.000000000&#x27;,\n",
+       "       &#x27;2022-03-14T23:58:00.000000000&#x27;, &#x27;2022-03-14T23:59:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-392c1458-5be1-400a-b5b6-6fa648df0c2f' class='xr-section-summary-in' type='checkbox'  ><label for='section-392c1458-5be1-400a-b5b6-6fa648df0c2f' class='xr-section-summary' >Data variables: <span>(22)</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>base_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-03-14</div><input id='attrs-3c93fd46-f263-4377-b1de-af2680aecf6d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c93fd46-f263-4377-b1de-af2680aecf6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47cedff1-6c25-492a-80d2-1df8022c1f87' class='xr-var-data-in' type='checkbox'><label for='data-47cedff1-6c25-492a-80d2-1df8022c1f87' 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>string :</span></dt><dd>2022-03-14 00:00:00 0:00</dd><dt><span>long_name :</span></dt><dd>Base time in Epoch</dd><dt><span>ancillary_variables :</span></dt><dd>time_offset</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2022-03-14T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_offset</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-03-14 ... 2022-03-14T23:59:00</div><input id='attrs-44e729bd-c0dd-48d5-bf7b-33b518f922e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44e729bd-c0dd-48d5-bf7b-33b518f922e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17a4cf09-9dbc-4471-b5f1-e7a2a46b56e6' class='xr-var-data-in' type='checkbox'><label for='data-17a4cf09-9dbc-4471-b5f1-e7a2a46b56e6' 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>long_name :</span></dt><dd>Time offset from base_time</dd><dt><span>ancillary_variables :</span></dt><dd>base_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-03-14T00:00:00.000000000&#x27;, &#x27;2022-03-14T00:01:00.000000000&#x27;,\n",
+       "       &#x27;2022-03-14T00:02:00.000000000&#x27;, ...,\n",
+       "       &#x27;2022-03-14T23:57:00.000000000&#x27;, &#x27;2022-03-14T23:58:00.000000000&#x27;,\n",
+       "       &#x27;2022-03-14T23:59:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intensity_rt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-44f0b796-9631-4cc5-81b5-630410c57204' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44f0b796-9631-4cc5-81b5-630410c57204' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6f6c450-f190-4757-ac7c-5625948baa84' class='xr-var-data-in' type='checkbox'><label for='data-d6f6c450-f190-4757-ac7c-5625948baa84' 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>long_name :</span></dt><dd>Heavy precipitation alarm</dd><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>ancillary_variables :</span></dt><dd>pluvio_status maintenance_flag reset_flag</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>3000.0</dd><dt><span>threshold :</span></dt><dd>6 mm/hr</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 6</dd><dt><span>comment_1 :</span></dt><dd>Only measurements that exceed the threshold are recorded. Any measurement below the threshold is reported as 0 mm/hr.</dd><dt><span>comment_2 :</span></dt><dd>The time bounds for the sum is [-60, 0]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\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>accum_rtnrt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-275ddb12-5691-474a-87fc-48acf27082bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-275ddb12-5691-474a-87fc-48acf27082bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4afe404-e774-4429-9dda-6c0bf853f163' class='xr-var-data-in' type='checkbox'><label for='data-f4afe404-e774-4429-9dda-6c0bf853f163' 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>long_name :</span></dt><dd>Accumulated amounts of precipitation over the sampling interval exceeding a threshold of 0.05mm or the accumulated amount of fine precipitation observed over the last hour</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>ancillary_variables :</span></dt><dd>pluvio_status maintenance_flag reset_flag</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>500.0</dd><dt><span>threshold :</span></dt><dd>0.05 mm</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 0.1</dd><dt><span>equation :</span></dt><dd>The accum_rtnrt variable is calculated by first measuring the accumulated amount of rain in the last minute. If this measurement exceeds the threshold, it reports this real time value. If the real time measurement does not reach the threshold, it reports the non real time measurement using the same equation as the accum_nrt variable.</dd><dt><span>comment :</span></dt><dd>Only measurements that exceed the threshold are recorded. Any measurement below the threshold is reported as 0 mm.</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\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>accum_nrt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-501c540e-2b18-42eb-9114-f97055926e83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-501c540e-2b18-42eb-9114-f97055926e83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16cbce88-c150-4c1e-a78a-e6aa8415d33c' class='xr-var-data-in' type='checkbox'><label for='data-16cbce88-c150-4c1e-a78a-e6aa8415d33c' 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>long_name :</span></dt><dd>Accumulated precipitation over the sampling interval filtered and delayed by 5 minute</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>ancillary_variables :</span></dt><dd>pluvio_status maintenance_flag reset_flag</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>500.0</dd><dt><span>threshold :</span></dt><dd>0.05 mm</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 0.1</dd><dt><span>equation :</span></dt><dd>The accum_nrt variable is calculated by measuring the amount of rain accumulate in a sampling interval at most 1 hour long, with the end of the interval at the given time. The start of the sampling interval occurs within the past hour, but is unknown. The start of the interval is determined once the accumulated sum either exceeds 0.05 or the interval length reaches an hour</dd><dt><span>comment :</span></dt><dd>Only measurements that exceed the threshold are recorded. Any measurement below the threshold is reported as 0 mm.</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accum_total_nrt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-b81b5e28-077f-45bc-9b47-6073386c4554' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b81b5e28-077f-45bc-9b47-6073386c4554' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e725834-9859-4f01-bbe6-1f9288f67307' class='xr-var-data-in' type='checkbox'><label for='data-2e725834-9859-4f01-bbe6-1f9288f67307' 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>long_name :</span></dt><dd>Sum of accum_nrt values since the last device start</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>ancillary_variables :</span></dt><dd>pluvio_status maintenance_flag reset_flag</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>500.0</dd><dt><span>threshold :</span></dt><dd>0.05 mm</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 0.1</dd><dt><span>comment :</span></dt><dd>Only measurements that exceed the threshold are recorded. Any measurement below the threshold is reported as 0 mm.</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bucket_rt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-0a7a6bcc-7214-436b-b9cb-f036487e2707' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a7a6bcc-7214-436b-b9cb-f036487e2707' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc98617d-891a-4be9-83bb-a7f9d3efd7d5' class='xr-var-data-in' type='checkbox'><label for='data-dc98617d-891a-4be9-83bb-a7f9d3efd7d5' 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>long_name :</span></dt><dd>The currently measured, unfiltered bucket contents since last reset</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>ancillary_variables :</span></dt><dd>pluvio_status maintenance_flag reset_flag</dd><dt><span>valid_min :</span></dt><dd>20.0</dd><dt><span>valid_max :</span></dt><dd>1800.0</dd><dt><span>threshold :</span></dt><dd>0.01 mm</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 0.1</dd><dt><span>comment :</span></dt><dd>Only increases that exceed the threshold are recorded. Any increase less than threshold is reported as no increase</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bucket_nrt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-27a2cf3a-5c65-4ae2-9758-419c658ed1bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27a2cf3a-5c65-4ae2-9758-419c658ed1bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4a9b68b-4628-4f40-8e2b-4aadee601313' class='xr-var-data-in' type='checkbox'><label for='data-a4a9b68b-4628-4f40-8e2b-4aadee601313' 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>long_name :</span></dt><dd>The currently measured, filtered bucket contents since last reset</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>ancillary_variables :</span></dt><dd>pluvio_status maintenance_flag reset_flag</dd><dt><span>valid_min :</span></dt><dd>20.0</dd><dt><span>valid_max :</span></dt><dd>1800.0</dd><dt><span>threshold :</span></dt><dd>0.01 mm</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 0.1</dd><dt><span>comment :</span></dt><dd>Only increases that exceed the threshold are recorded. Any increase less than threshold is reported as no increase</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\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>load_cell_temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-fcd8998f-b1b9-4565-b5a6-54fdb8107a0f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fcd8998f-b1b9-4565-b5a6-54fdb8107a0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f399cac1-a435-4663-b306-dc27ee3d9b20' class='xr-var-data-in' type='checkbox'><label for='data-f399cac1-a435-4663-b306-dc27ee3d9b20' 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>long_name :</span></dt><dd>Temperature of load cell</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>valid_min :</span></dt><dd>-50.0</dd><dt><span>valid_max :</span></dt><dd>70.0</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\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>heater_status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-1a3cbf38-4d4a-4900-9c55-3837357adc80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a3cbf38-4d4a-4900-9c55-3837357adc80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a9cbc65-a7f8-4761-b1f9-6f1f9397288a' class='xr-var-data-in' type='checkbox'><label for='data-7a9cbc65-a7f8-4761-b1f9-6f1f9397288a' 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>long_name :</span></dt><dd>Heater status</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>comment :</span></dt><dd>This flag can indicate several statuses. This will be reflected as the sum of the status values: 0 indicates heater is operating properly</dd><dt><span>flag_masks :</span></dt><dd>[  1   2   4   8  16  32 128]</dd><dt><span>flag_meanings :</span></dt><dd>orifice_rim_temp_above_40C orifice_rim_temp_below_neg_20C temp_sensor_not_connected temp_sensor_short_circuit comm_to_heating_module_defective_or_instr_housing_removed func_check_defective deactivated</dd><dt><span>bit_1_description :</span></dt><dd>orifice rim temperature &gt; 40 degC</dd><dt><span>bit_2_description :</span></dt><dd>orifice rim temperature &lt; -20 degC</dd><dt><span>bit_3_description :</span></dt><dd>temperature sensor not connected</dd><dt><span>bit_4_description :</span></dt><dd>temperature sensor short circuit</dd><dt><span>bit_5_description :</span></dt><dd>communication to ring heating module defective or instrument housing was removed</dd><dt><span>bit_6_description :</span></dt><dd>functional check of orifice rim was defective</dd><dt><span>bit_8_description :</span></dt><dd>orifice heating deactivated</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 11.25 kiB </td>\n",
+       "                        <td> 11.25 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\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>pluvio_status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-40544379-4706-41dd-a61e-c1f3367c3701' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40544379-4706-41dd-a61e-c1f3367c3701' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d2fac40-f46a-46e2-889d-dddd0dc88334' class='xr-var-data-in' type='checkbox'><label for='data-4d2fac40-f46a-46e2-889d-dddd0dc88334' 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>long_name :</span></dt><dd>Pluvio status</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>comment :</span></dt><dd>This flag can indicate several statuses. This will be reflected as the sum of the status values. 0 indicates gauge is operating properly</dd><dt><span>flag_masks :</span></dt><dd>[   1    2    4    8   16   32   64  128  256  512 1024]</dd><dt><span>flag_meanings :</span></dt><dd>bucket_fill_level_above_80_percent USB_interface_connected restart_power_failure restart_firmware weight_change_not_permitted volt_supply_below_7V weight_measure_unstable weight_measure_defective weight_below_min weight_above_max no_weight_calib</dd><dt><span>bit_1_description :</span></dt><dd>bucket fill level &gt; 80%</dd><dt><span>bit_2_description :</span></dt><dd>USB interface is/was connected</dd><dt><span>bit_3_description :</span></dt><dd>restart due to power failure</dd><dt><span>bit_4_description :</span></dt><dd>restart due to firmware</dd><dt><span>bit_5_description :</span></dt><dd>weight change not permitted</dd><dt><span>bit_6_description :</span></dt><dd>voltage supply &lt; 7V</dd><dt><span>bit_7_description :</span></dt><dd>weight measurement unstable</dd><dt><span>bit_8_description :</span></dt><dd>weight measurement defective</dd><dt><span>bit_9_description :</span></dt><dd>weight less than minimum</dd><dt><span>bit_10_description :</span></dt><dd>weight greater than maximum</dd><dt><span>bit_11_description :</span></dt><dd>no weight calibration</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 11.25 kiB </td>\n",
+       "                        <td> 11.25 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\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>elec_unit_temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-b074ceb0-3137-487f-aa8c-61130ac0f981' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b074ceb0-3137-487f-aa8c-61130ac0f981' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-538253a1-f780-49c1-8fb9-d99211c4c1f5' class='xr-var-data-in' type='checkbox'><label for='data-538253a1-f780-49c1-8fb9-d99211c4c1f5' 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>long_name :</span></dt><dd>Temperature of electronics unit</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>valid_min :</span></dt><dd>-50.0</dd><dt><span>valid_max :</span></dt><dd>70.0</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
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+       "\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>supply_volts</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-6477ddba-4686-4f00-a935-78900e5db533' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6477ddba-4686-4f00-a935-78900e5db533' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efbc673f-f0f3-446f-b7ee-9d428764f3dc' class='xr-var-data-in' type='checkbox'><label for='data-efbc673f-f0f3-446f-b7ee-9d428764f3dc' 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>long_name :</span></dt><dd>Supply voltage</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>valid_min :</span></dt><dd>4.5</dd><dt><span>valid_max :</span></dt><dd>28.0</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 0.5</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\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>orifice_temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-56309ed0-c802-4ef3-953c-4ecbed210517' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56309ed0-c802-4ef3-953c-4ecbed210517' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3a917ee-7364-49f5-afa5-85f98ee28d85' class='xr-var-data-in' type='checkbox'><label for='data-e3a917ee-7364-49f5-afa5-85f98ee28d85' 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>long_name :</span></dt><dd>Temperature of orifice rim</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>ancillary_variables :</span></dt><dd>heater_status</dd><dt><span>valid_min :</span></dt><dd>-50.0</dd><dt><span>valid_max :</span></dt><dd>70.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\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>maintenance_flag</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-861e791a-fc4c-4ae8-8c23-bb36662da97d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-861e791a-fc4c-4ae8-8c23-bb36662da97d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4935ea35-3faa-466f-8ca0-4411a78a4304' class='xr-var-data-in' type='checkbox'><label for='data-4935ea35-3faa-466f-8ca0-4411a78a4304' 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>long_name :</span></dt><dd>Bucket is being emptied or serviced</dd><dt><span>units :</span></dt><dd>count</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>20</dd><dt><span>comment :</span></dt><dd>If the flag is not 0, then the instrument is being serviced or the bucket is being empty. This only occurs when it is not raining. Therefore, if the flag is not 0, then it is not raining and any values for the bucket or accum variables should be ignored</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 11.25 kiB </td>\n",
+       "                        <td> 11.25 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\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>reset_flag</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-755e383b-ea72-4bf1-8d84-a3ed631fe861' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-755e383b-ea72-4bf1-8d84-a3ed631fe861' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b4eec61-cbb7-4084-af2c-c4448c4aa046' class='xr-var-data-in' type='checkbox'><label for='data-8b4eec61-cbb7-4084-af2c-c4448c4aa046' 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>long_name :</span></dt><dd>Bucket emptied</dd><dt><span>units :</span></dt><dd>count</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>20</dd><dt><span>comment :</span></dt><dd>If the flag is not 0, then the bucket on the instrument is being empty. This only occurs when it is not raining. Therefore, if the flag is not 0, then it is not raining and any values for the bucket or accum variables should be ignored</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\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>volt_min</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-c765e045-60dd-4843-8af5-90bf565c9497' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c765e045-60dd-4843-8af5-90bf565c9497' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6804b6b-59af-415f-bb8d-27a8d56ddc4d' class='xr-var-data-in' type='checkbox'><label for='data-a6804b6b-59af-415f-bb8d-27a8d56ddc4d' 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>long_name :</span></dt><dd>Minimum supply voltage of logger</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>20.0</dd><dt><span>comment :</span></dt><dd>The time bounds for the minimum is [-60,0]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\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>ptemp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-445ca666-338e-46e3-8510-648c3630abc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-445ca666-338e-46e3-8510-648c3630abc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09814c0c-578d-4542-9f54-3c7aa7e72c3a' class='xr-var-data-in' type='checkbox'><label for='data-09814c0c-578d-4542-9f54-3c7aa7e72c3a' 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>long_name :</span></dt><dd>Panel temperature average</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>valid_min :</span></dt><dd>-50.0</dd><dt><span>valid_max :</span></dt><dd>70.0</dd><dt><span>comment :</span></dt><dd>The time bounds for the average is [-60, 0]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intensity_rtnrt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-a7310412-cb94-4a60-a63f-9f6210d13c4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7310412-cb94-4a60-a63f-9f6210d13c4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f6e4b6b-d531-4e4f-b1c0-cce315ec822b' class='xr-var-data-in' type='checkbox'><label for='data-4f6e4b6b-d531-4e4f-b1c0-cce315ec822b' 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>long_name :</span></dt><dd>Rain intensity based upon accum_rtnrt</dd><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>ancillary_variables :</span></dt><dd>pluvio_status maintenance_flag reset_flag</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>30000.0</dd><dt><span>threshold :</span></dt><dd>0.3 mm/hr</dd><dt><span>absolute_accuracy :</span></dt><dd>plus/minus 6</dd><dt><span>equation :</span></dt><dd>Calculated by accum_rtnrt * 60</dd><dt><span>comment :</span></dt><dd>Only measurements that exceed the threshold are recorded. Any measurement below the threshold is reported as 0 mm/hr.</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\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> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6e3d6854-a5b6-4771-b2fa-fcf5849fb47f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e3d6854-a5b6-4771-b2fa-fcf5849fb47f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88a97cf0-f718-4d25-8944-a1685062b326' class='xr-var-data-in' type='checkbox'><label for='data-88a97cf0-f718-4d25-8944-a1685062b326' 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>long_name :</span></dt><dd>North latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-badc76e2-e197-46af-90ab-706fbc916889' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-badc76e2-e197-46af-90ab-706fbc916889' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d316cca1-de4a-4fd2-9719-724dee46da50' class='xr-var-data-in' type='checkbox'><label for='data-d316cca1-de4a-4fd2-9719-724dee46da50' 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>long_name :</span></dt><dd>East longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-89d93f7f-52f2-4687-ae4b-48ea8b6dd9ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89d93f7f-52f2-4687-ae4b-48ea8b6dd9ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5ff6f60-c8ea-4a9f-bdbd-21ccedeff877' class='xr-var-data-in' type='checkbox'><label for='data-f5ff6f60-c8ea-4a9f-bdbd-21ccedeff877' 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>long_name :</span></dt><dd>Altitude above mean sea level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>altitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c4388a8c-92af-4691-9da1-c84aa3961ee8' class='xr-section-summary-in' type='checkbox'  ><label for='section-c4388a8c-92af-4691-9da1-c84aa3961ee8' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a2f869ff-81cb-4243-b3c8-54f384edd0bf' class='xr-index-data-in' type='checkbox'/><label for='index-a2f869ff-81cb-4243-b3c8-54f384edd0bf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-03-14 00:00:00&#x27;, &#x27;2022-03-14 00:01:00&#x27;,\n",
+       "               &#x27;2022-03-14 00:02:00&#x27;, &#x27;2022-03-14 00:03:00&#x27;,\n",
+       "               &#x27;2022-03-14 00:04:00&#x27;, &#x27;2022-03-14 00:05:00&#x27;,\n",
+       "               &#x27;2022-03-14 00:06:00&#x27;, &#x27;2022-03-14 00:07:00&#x27;,\n",
+       "               &#x27;2022-03-14 00:08:00&#x27;, &#x27;2022-03-14 00:09:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2022-03-14 23:50:00&#x27;, &#x27;2022-03-14 23:51:00&#x27;,\n",
+       "               &#x27;2022-03-14 23:52:00&#x27;, &#x27;2022-03-14 23:53:00&#x27;,\n",
+       "               &#x27;2022-03-14 23:54:00&#x27;, &#x27;2022-03-14 23:55:00&#x27;,\n",
+       "               &#x27;2022-03-14 23:56:00&#x27;, &#x27;2022-03-14 23:57:00&#x27;,\n",
+       "               &#x27;2022-03-14 23:58:00&#x27;, &#x27;2022-03-14 23:59:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1440, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0a4d8db8-f5cc-4056-837f-a9b95ecd2890' class='xr-section-summary-in' type='checkbox'  ><label for='section-0a4d8db8-f5cc-4056-837f-a9b95ecd2890' class='xr-section-summary' >Attributes: <span>(19)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>command_line :</span></dt><dd>wbpluvio2_ingest -s guc -f M1</dd><dt><span>Conventions :</span></dt><dd>ARM-1.2</dd><dt><span>process_version :</span></dt><dd>ingest-wbpluvio2-1.1-0.el7</dd><dt><span>dod_version :</span></dt><dd>wbpluvio2-a1-1.0</dd><dt><span>input_source :</span></dt><dd>/data/collection/guc/gucwbpluvio2M1.00/DL2.wbpluvio2.20220314000000.dat</dd><dt><span>site_id :</span></dt><dd>guc</dd><dt><span>platform_id :</span></dt><dd>wbpluvio2</dd><dt><span>facility_id :</span></dt><dd>M1</dd><dt><span>data_level :</span></dt><dd>a1</dd><dt><span>location_description :</span></dt><dd>Surface Atmosphere Integrated Field Laboratory (SAIL), Mt Crested Butte, Colorado</dd><dt><span>datastream :</span></dt><dd>gucwbpluvio2M1.a1</dd><dt><span>serial_number :</span></dt><dd>398905</dd><dt><span>sampling_interval :</span></dt><dd>1 minute</dd><dt><span>doi :</span></dt><dd>10.5439/1338194</dd><dt><span>history :</span></dt><dd>created by user dsmgr on machine flint at 2022-03-14 02:05:01, using ingest-wbpluvio2-1.1-0.el7</dd><dt><span>_file_dates :</span></dt><dd>[&#x27;20220314&#x27;]</dd><dt><span>_file_times :</span></dt><dd>[&#x27;000000&#x27;]</dd><dt><span>_datastream :</span></dt><dd>gucwbpluvio2M1.a1</dd><dt><span>_arm_standards_flag :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:           (time: 1440)\n",
+       "Coordinates:\n",
+       "  * time              (time) datetime64[ns] 2022-03-14 ... 2022-03-14T23:59:00\n",
+       "Data variables: (12/22)\n",
+       "    base_time         datetime64[ns] 2022-03-14\n",
+       "    time_offset       (time) datetime64[ns] 2022-03-14 ... 2022-03-14T23:59:00\n",
+       "    intensity_rt      (time) float32 dask.array<chunksize=(1440,), meta=np.ndarray>\n",
+       "    accum_rtnrt       (time) float32 dask.array<chunksize=(1440,), meta=np.ndarray>\n",
+       "    accum_nrt         (time) float32 dask.array<chunksize=(1440,), meta=np.ndarray>\n",
+       "    accum_total_nrt   (time) float32 dask.array<chunksize=(1440,), meta=np.ndarray>\n",
+       "    ...                ...\n",
+       "    volt_min          (time) float32 dask.array<chunksize=(1440,), meta=np.ndarray>\n",
+       "    ptemp             (time) float32 dask.array<chunksize=(1440,), meta=np.ndarray>\n",
+       "    intensity_rtnrt   (time) float32 dask.array<chunksize=(1440,), meta=np.ndarray>\n",
+       "    lat               float32 ...\n",
+       "    lon               float32 ...\n",
+       "    alt               float32 ...\n",
+       "Attributes: (12/19)\n",
+       "    command_line:          wbpluvio2_ingest -s guc -f M1\n",
+       "    Conventions:           ARM-1.2\n",
+       "    process_version:       ingest-wbpluvio2-1.1-0.el7\n",
+       "    dod_version:           wbpluvio2-a1-1.0\n",
+       "    input_source:          /data/collection/guc/gucwbpluvio2M1.00/DL2.wbpluvi...\n",
+       "    site_id:               guc\n",
+       "    ...                    ...\n",
+       "    doi:                   10.5439/1338194\n",
+       "    history:               created by user dsmgr on machine flint at 2022-03-...\n",
+       "    _file_dates:           ['20220314']\n",
+       "    _file_times:           ['000000']\n",
+       "    _datastream:           gucwbpluvio2M1.a1\n",
+       "    _arm_standards_flag:   1"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gauge_ds = act.io.read_netcdf(files)\n",
+    "gauge_ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize Where the Rain Gauge is Located\n",
+    "The gauge dataset has latitude/longitude information encoded as variables! We can use this information to plot it on the map."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array(38.956158, dtype=float32), array(-106.987854, dtype=float32))"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gauge_lat = gauge_ds.lat.values\n",
+    "gauge_lon = gauge_ds.lon.values\n",
+    "(gauge_lat, gauge_lon)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have our gauge location, we can plot with the snow rate data!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "squire_ds.snow_rate_ws2012.plot(x='lon',\n",
+    "                                y='lat',\n",
+    "                                cmap='ChaseSpectral');\n",
+    "plt.scatter(gauge_lon,\n",
+    "            gauge_lat,\n",
+    "            color='r');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Subset the SQUIRE dataset for the Gauge Location\n",
+    "We have the location of the gauge, as well as liquid precipitation rates for the datasets. Let's compare them by **subsetting** the gauge location from the gridded dataset. We first need to apply data cleaning, swapping the dimensions x/y with lat/lon to make it easier to select our point of interest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if \"lat\" not in squire_ds.dims:\n",
+    "    squire_ds = squire_ds.swap_dims({'y': 'lat',\n",
+    "                                     'x': 'lon'})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we have dataset location, we use the `.sel()` method to extract the closest grid point to the gauge location."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  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",
+       ".xr-index-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",
+       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-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",
+       ".xr-no-icon {\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: 1)\n",
+       "Coordinates:\n",
+       "  * time                    (time) datetime64[ns] 2022-03-14T02:42:39\n",
+       "    lat                     float64 38.96\n",
+       "    y                       float64 6.5e+03\n",
+       "    lon                     float64 -107.0\n",
+       "    x                       float64 -3.75e+03\n",
+       "    z                       (time) float64 500.0\n",
+       "Data variables:\n",
+       "    DBZ                     (time) float64 24.95\n",
+       "    corrected_reflectivity  (time) float64 24.95\n",
+       "    rain_rate_A             (time) float64 0.0\n",
+       "    snow_rate_ws88diw       (time) float64 2.827\n",
+       "    snow_rate_m2009_1       (time) float64 3.421\n",
+       "    snow_rate_m2009_2       (time) float64 2.113\n",
+       "    snow_rate_ws2012        (time) float64 1.705\n",
+       "    lowest_height           (time) float64 500.0\n",
+       "Attributes: (12/24)\n",
+       "    command_line:          python run_squire.py\n",
+       "    Conventions:           ARM-1.3 CF/Radial instrument_parameters\n",
+       "    process_version:       SQUIRE 1.0\n",
+       "    dod_version:           xprecipradarsquire-c1-1.0\n",
+       "    site_id:               guc\n",
+       "    platform_id:           xprecipradarsquire\n",
+       "    ...                    ...\n",
+       "    mentors:               https://www.arm.gov/connect-with-arm/organization/...\n",
+       "    source:                Colorado State University&#x27;s X-Band Precipitation R...\n",
+       "    input_datastreams:     gucxprecipradarcmacppiS2.c1\n",
+       "    fields:                DBZ, corrected_reflectivity, time, lowest_height, ...\n",
+       "    history:               created by mgrover4 on Cumulus at 2023-02-06 19:10...\n",
+       "    DOD:                   gucxprecipradarsquire.c1</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-88dc0339-ecaa-4658-abb6-d817e185b1b4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-88dc0339-ecaa-4658-abb6-d817e185b1b4' 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>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-690ce585-27cc-4ac3-815c-eae9f9090e93' class='xr-section-summary-in' type='checkbox'  checked><label for='section-690ce585-27cc-4ac3-815c-eae9f9090e93' 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'>2022-03-14T02:42:39</div><input id='attrs-f32c4361-9f81-4eb4-b8ce-45fefb91ac04' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f32c4361-9f81-4eb4-b8ce-45fefb91ac04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a59ddead-f1f0-4be0-a825-5afe1717fe30' class='xr-var-data-in' type='checkbox'><label for='data-a59ddead-f1f0-4be0-a825-5afe1717fe30' 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;2022-03-14T02:42:39.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>38.96</div><input id='attrs-038b14ba-b229-4f60-9b1f-db27160875f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-038b14ba-b229-4f60-9b1f-db27160875f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36ea00f7-60c4-4dc5-b65d-57ef079eda59' class='xr-var-data-in' type='checkbox'><label for='data-36ea00f7-60c4-4dc5-b65d-57ef079eda59' 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>long_name :</span></dt><dd>North latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>valid_min :</span></dt><dd>-90</dd><dt><span>valid_max :</span></dt><dd>90</dd></dl></div><div class='xr-var-data'><pre>array(38.95660814)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.5e+03</div><input id='attrs-0cc64188-74e6-411c-9027-1bb1edac2bef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cc64188-74e6-411c-9027-1bb1edac2bef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be21609a-d745-45f0-84d3-50f815d24853' class='xr-var-data-in' type='checkbox'><label for='data-be21609a-d745-45f0-84d3-50f815d24853' 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>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>long_name :</span></dt><dd>Y distance on the projection plane from the origin</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array(6500.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-107.0</div><input id='attrs-57cc4634-640b-475e-9a59-c4285e53bc56' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57cc4634-640b-475e-9a59-c4285e53bc56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53431f65-0d8d-47e3-bfbf-93b81e09ab90' class='xr-var-data-in' type='checkbox'><label for='data-53431f65-0d8d-47e3-bfbf-93b81e09ab90' 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>long_name :</span></dt><dd>East longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>valid_min :</span></dt><dd>-180</dd><dt><span>valid_max :</span></dt><dd>180</dd></dl></div><div class='xr-var-data'><pre>array(-106.98643833)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-3.75e+03</div><input id='attrs-0783236f-2096-4be6-b7f1-a72d88b61ea5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0783236f-2096-4be6-b7f1-a72d88b61ea5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adf6903e-c5d1-46a2-ad34-cf9bd7c16a58' class='xr-var-data-in' type='checkbox'><label for='data-adf6903e-c5d1-46a2-ad34-cf9bd7c16a58' 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>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>long_name :</span></dt><dd>X distance on the projection plane from the origin</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array(-3750.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>500.0</div><input id='attrs-b3fb591d-8070-46ed-af00-ed4b64d67c91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3fb591d-8070-46ed-af00-ed4b64d67c91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-890c98fd-4d47-4f4b-aa38-0ab2e4cedc04' class='xr-var-data-in' type='checkbox'><label for='data-890c98fd-4d47-4f4b-aa38-0ab2e4cedc04' 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>long_name :</span></dt><dd>Z distance on the projection plane from the origin</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>projection_z_coordinate</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array([500.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-187c0b7e-093d-482e-9571-6d8a0fc9ff32' class='xr-section-summary-in' type='checkbox'  checked><label for='section-187c0b7e-093d-482e-9571-6d8a0fc9ff32' class='xr-section-summary' >Data variables: <span>(8)</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>DBZ</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>24.95</div><input id='attrs-ebb5b4b6-e8b9-4a23-b13c-bcf76c4f7109' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ebb5b4b6-e8b9-4a23-b13c-bcf76c4f7109' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-865cb294-4e12-40d9-b1d9-5b0e471d04c7' class='xr-var-data-in' type='checkbox'><label for='data-865cb294-4e12-40d9-b1d9-5b0e471d04c7' 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>long_name :</span></dt><dd>Equivalent Radar Reflectivity Factor</dd><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>standard_name :</span></dt><dd>equivalent_reflectivity_factor</dd></dl></div><div class='xr-var-data'><pre>array([24.95020103])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corrected_reflectivity</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>24.95</div><input id='attrs-b3c2bbbc-d68b-452b-afac-63c69e8b9253' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3c2bbbc-d68b-452b-afac-63c69e8b9253' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df7db289-b425-426d-8496-acc4f7f92805' class='xr-var-data-in' type='checkbox'><label for='data-df7db289-b425-426d-8496-acc4f7f92805' 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>long_name :</span></dt><dd>Corrected reflectivity</dd><dt><span>units :</span></dt><dd>dBZ</dd><dt><span>standard_name :</span></dt><dd>corrected_equivalent_reflectivity_factor</dd></dl></div><div class='xr-var-data'><pre>array([24.95020103])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain_rate_A</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-968992d4-1eac-466f-b09d-160f9bb0b8b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-968992d4-1eac-466f-b09d-160f9bb0b8b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64b1ab4e-b96c-4310-8cf5-6727c4593ade' class='xr-var-data-in' type='checkbox'><label for='data-64b1ab4e-b96c-4310-8cf5-6727c4593ade' 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>long_name :</span></dt><dd>Rainfall Rate from Specific Attenuation</dd><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>comment :</span></dt><dd>Rain rate calculated from specific_attenuation, R=43.5*specific_attenuation**0.79, note R=0.0 where norm coherent power &lt; 0.4 or rhohv &lt; 0.8</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>400.0</dd><dt><span>coordinates :</span></dt><dd>elevation azimuth range</dd><dt><span>standard_name :</span></dt><dd>rainfall_rate</dd></dl></div><div class='xr-var-data'><pre>array([0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_rate_ws88diw</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.827</div><input id='attrs-b9fa1bf0-fb5c-426d-b2f7-d784a0e35864' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9fa1bf0-fb5c-426d-b2f7-d784a0e35864' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72e169ce-2a20-4791-8f09-9af1f73ee86a' class='xr-var-data-in' type='checkbox'><label for='data-72e169ce-2a20-4791-8f09-9af1f73ee86a' 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>long_name :</span></dt><dd>Snowfall rate from Z using WSR 88D High Plains</dd><dt><span>units :</span></dt><dd>mm/h</dd><dt><span>standard_name :</span></dt><dd>snowfall_rate</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>500</dd><dt><span>swe_ratio :</span></dt><dd>13.699</dd><dt><span>A :</span></dt><dd>40</dd><dt><span>B :</span></dt><dd>2</dd></dl></div><div class='xr-var-data'><pre>array([2.82662845])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_rate_m2009_1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.421</div><input id='attrs-906cf6f5-e615-4f93-8343-73805e8a459c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-906cf6f5-e615-4f93-8343-73805e8a459c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f231ba7e-1c72-4c05-ae3c-c7b8389ccd7b' class='xr-var-data-in' type='checkbox'><label for='data-f231ba7e-1c72-4c05-ae3c-c7b8389ccd7b' 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>long_name :</span></dt><dd>Snowfall rate from Z using Matrosov et al.(2009) Braham(1990) 1</dd><dt><span>units :</span></dt><dd>mm/h</dd><dt><span>standard_name :</span></dt><dd>snowfall_rate</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>500</dd><dt><span>swe_ratio :</span></dt><dd>13.699</dd><dt><span>A :</span></dt><dd>67</dd><dt><span>B :</span></dt><dd>1.28</dd></dl></div><div class='xr-var-data'><pre>array([3.42096353])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_rate_m2009_2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.113</div><input id='attrs-e831f3a2-b8a9-4951-a724-6268be326763' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e831f3a2-b8a9-4951-a724-6268be326763' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87dedc5b-9378-47a8-a257-65a5360c0385' class='xr-var-data-in' type='checkbox'><label for='data-87dedc5b-9378-47a8-a257-65a5360c0385' 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>long_name :</span></dt><dd>Snowfall rate from Z using Matrosov et al.(2009) Braham(1990) 2</dd><dt><span>units :</span></dt><dd>mm/h</dd><dt><span>standard_name :</span></dt><dd>snowfall_rate</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>500</dd><dt><span>swe_ratio :</span></dt><dd>13.699</dd><dt><span>A :</span></dt><dd>114</dd><dt><span>B :</span></dt><dd>1.39</dd></dl></div><div class='xr-var-data'><pre>array([2.11349177])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_rate_ws2012</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.705</div><input id='attrs-58cbcd26-a1f7-4a01-850b-882380ed60d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58cbcd26-a1f7-4a01-850b-882380ed60d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9178b7f-871b-47c4-8c17-346ca516ac5f' class='xr-var-data-in' type='checkbox'><label for='data-f9178b7f-871b-47c4-8c17-346ca516ac5f' 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>long_name :</span></dt><dd>Snowfall rate from Z using Wolf and Snider (2012)</dd><dt><span>units :</span></dt><dd>mm/h</dd><dt><span>standard_name :</span></dt><dd>snowfall_rate</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>500</dd><dt><span>swe_ratio :</span></dt><dd>13.699</dd><dt><span>A :</span></dt><dd>110</dd><dt><span>B :</span></dt><dd>2</dd></dl></div><div class='xr-var-data'><pre>array([1.70452142])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lowest_height</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>500.0</div><input id='attrs-903c879c-5f0e-458b-9e1a-b9610d1a82d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-903c879c-5f0e-458b-9e1a-b9610d1a82d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f9a7baf-c157-4cd5-8841-dea146b1e29f' class='xr-var-data-in' type='checkbox'><label for='data-7f9a7baf-c157-4cd5-8841-dea146b1e29f' 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>long_name :</span></dt><dd>Height of the lowest Radar Gate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array([500.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f368ef98-5a46-4dc4-b70e-e4a0f637028c' class='xr-section-summary-in' type='checkbox'  ><label for='section-f368ef98-5a46-4dc4-b70e-e4a0f637028c' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-429cab84-9b72-4f98-9b44-a833474b97b7' class='xr-index-data-in' type='checkbox'/><label for='index-429cab84-9b72-4f98-9b44-a833474b97b7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-03-14 02:42:39&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-44d73b36-4096-4399-a217-b0a8cce0bac3' class='xr-section-summary-in' type='checkbox'  ><label for='section-44d73b36-4096-4399-a217-b0a8cce0bac3' class='xr-section-summary' >Attributes: <span>(24)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>command_line :</span></dt><dd>python run_squire.py</dd><dt><span>Conventions :</span></dt><dd>ARM-1.3 CF/Radial instrument_parameters</dd><dt><span>process_version :</span></dt><dd>SQUIRE 1.0</dd><dt><span>dod_version :</span></dt><dd>xprecipradarsquire-c1-1.0</dd><dt><span>site_id :</span></dt><dd>guc</dd><dt><span>platform_id :</span></dt><dd>xprecipradarsquire</dd><dt><span>facility_id :</span></dt><dd>S2</dd><dt><span>data_level :</span></dt><dd>c1</dd><dt><span>location_description :</span></dt><dd>Gunnison, CO; Surface Atmosphere Integrated field Laboratory (GUC)</dd><dt><span>datastream :</span></dt><dd>gucxprecipradarsquireS2.c1</dd><dt><span>institution :</span></dt><dd>U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Climate Research Facility</dd><dt><span>references :</span></dt><dd>See XPRECIPRADAR Instrument Handbook</dd><dt><span>doi :</span></dt><dd>10.5439/1884979</dd><dt><span>comment :</span></dt><dd>This is highly experimental and initial data. There are many known and unknown issues. Please do not use before contacting the Translator responsible scollis@anl.gov</dd><dt><span>attributions :</span></dt><dd>This data is collected by the ARM Climate Research facility. Radar system is operated by the radar engineering team radar@arm.gov and the data is processed by the precipitation radar products team. LP code courtesy of Scott Giangrande BNL.</dd><dt><span>known_issues :</span></dt><dd>False phidp jumps in insect regions. Still uses old Giangrande code. Issues with some snow below melting layer.</dd><dt><span>developers :</span></dt><dd>Maxwell Grover, ANL. Joseph O&#x27;Brien, ANL. Robert Jackson, ANL. Zachary Sherman, ANL.</dd><dt><span>translator :</span></dt><dd>https://www.arm.gov/capabilities/instruments/xprecipradar</dd><dt><span>mentors :</span></dt><dd>https://www.arm.gov/connect-with-arm/organization/instrument-mentors/list#xprecipradar</dd><dt><span>source :</span></dt><dd>Colorado State University&#x27;s X-Band Precipitation Radar (XPRECIPRADAR) (DOI: 10.5439/1844501)</dd><dt><span>input_datastreams :</span></dt><dd>gucxprecipradarcmacppiS2.c1</dd><dt><span>fields :</span></dt><dd>DBZ, corrected_reflectivity, time, lowest_height, rain_rate_A, snow_rate_ws88diw, snow_rate_m2009_1, snow_rate_m2009_2, snow_rate_ws2012, z, y, x, lat, lon</dd><dt><span>history :</span></dt><dd>created by mgrover4 on Cumulus at 2023-02-06 19:10:49.47 using Py-ART</dd><dt><span>DOD :</span></dt><dd>gucxprecipradarsquire.c1</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:                 (time: 1)\n",
+       "Coordinates:\n",
+       "  * time                    (time) datetime64[ns] 2022-03-14T02:42:39\n",
+       "    lat                     float64 38.96\n",
+       "    y                       float64 6.5e+03\n",
+       "    lon                     float64 -107.0\n",
+       "    x                       float64 -3.75e+03\n",
+       "    z                       (time) float64 500.0\n",
+       "Data variables:\n",
+       "    DBZ                     (time) float64 24.95\n",
+       "    corrected_reflectivity  (time) float64 24.95\n",
+       "    rain_rate_A             (time) float64 0.0\n",
+       "    snow_rate_ws88diw       (time) float64 2.827\n",
+       "    snow_rate_m2009_1       (time) float64 3.421\n",
+       "    snow_rate_m2009_2       (time) float64 2.113\n",
+       "    snow_rate_ws2012        (time) float64 1.705\n",
+       "    lowest_height           (time) float64 500.0\n",
+       "Attributes: (12/24)\n",
+       "    command_line:          python run_squire.py\n",
+       "    Conventions:           ARM-1.3 CF/Radial instrument_parameters\n",
+       "    process_version:       SQUIRE 1.0\n",
+       "    dod_version:           xprecipradarsquire-c1-1.0\n",
+       "    site_id:               guc\n",
+       "    platform_id:           xprecipradarsquire\n",
+       "    ...                    ...\n",
+       "    mentors:               https://www.arm.gov/connect-with-arm/organization/...\n",
+       "    source:                Colorado State University's X-Band Precipitation R...\n",
+       "    input_datastreams:     gucxprecipradarcmacppiS2.c1\n",
+       "    fields:                DBZ, corrected_reflectivity, time, lowest_height, ...\n",
+       "    history:               created by mgrover4 on Cumulus at 2023-02-06 19:10...\n",
+       "    DOD:                   gucxprecipradarsquire.c1"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "squire_at_gauge = squire_ds.sel(lat=gauge_lat,\n",
+    "                                lon=gauge_lon,\n",
+    "                                method='nearest').compute()\n",
+    "squire_at_gauge"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition alert alert-success\">\n",
+    "    <p class=\"admonition-title\" style=\"font-weight:bold\">We did it!</p>\n",
+    "    We successfully extracted precipitation rates for our site!\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot our Results\n",
+    "The last step is to plot our results. We first define which color to plot our different relationships using a dictionary! then, we setup a figure and plot our features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "color_dict = {\"snow_rate_ws88diw\": \"tab:green\",\n",
+    "              \"snow_rate_m2009_1\": \"tab:orange\",\n",
+    "              \"snow_rate_m2009_2\": \"tab:blue\",\n",
+    "              \"snow_rate_ws2012\": \"tab:red\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "volume_scan_time = \"2022-03-14T02:41:00.000000000\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(8,4))\n",
+    "ax = plt.subplot(111)\n",
+    "\n",
+    "# Plot the 30-minute time of interest\n",
+    "gauge_ds.sel(time=slice('2022-03-14T02:30:00.000000000',\n",
+    "                  '2022-03-14T03:00:00.000000000')\n",
+    "      ).intensity_rtnrt.plot(ax=ax)\n",
+    "\n",
+    "ax.axvline(volume_scan_time,\n",
+    "            label='Volume Scan Time',\n",
+    "            color='k'\n",
+    "          )\n",
+    "\n",
+    "# Plot each Z(S) relationship as a horizontal line\n",
+    "for variable in list(squire_at_gauge.variables):\n",
+    "    if \"snow_rate\" in variable:\n",
+    "        z_s = f\"{variable}: $Z = {squire_ds[variable].A}S^\" + \"{\" + f\"{squire_ds[variable].B}\" + \"}$\"\n",
+    "        ax.scatter(volume_scan_time,\n",
+    "                   squire_at_gauge[variable],\n",
+    "                   label=z_s,\n",
+    "                   color=color_dict[variable],\n",
+    "                   linestyle=\":\")\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.title(\"Liquid Precipitation Rate Comparison \\n 0242 UTC March 14, 2022\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition alert alert-danger\">\n",
+    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Danger</p>\n",
+    "    Be careful - this is not a definitive comparison, more case comparisons/case studies are required to understand which Z(S) relationship performed the best\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "In this notebook, we explored the SQUIRE dataset, which is a Value-Added product produced by the ARM program. This dataset is helpful with the SAIL field campaign, especially for those interested in precipitation estimates near complex terrain.\n",
+    "\n",
+    "We plotted data from a single case, and compared with nearby observations to estimate how well the precipitation rates compare."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Resources and references\n",
+    "* [SQUIRE Documentation](https://www.arm.gov/capabilities/science-data-products/vaps/squire)\n",
+    "* [SQUIRE Technical Document](https://www.arm.gov/publications/tech_reports/doe-sc-arm-tr-287.pdf)\n",
+    "* Matrosov SY, C Campbell, D Kingsmill, and E Sukovich. 2009. “Assessing Snowfall Rates from X-Band Radar Reflectivity Measurements.” Journal of Atmospheric and Oceanic Technology, 26, 2324–2339, doi:10.1175/2009JTECHA1238.1\n",
+    "\n",
+    "* Braham RR. 1990. “Snow Particle Size Spectra in Lake Effect Snows”. Journal of Applied Meteorology and Climatology, 29, 200–207, doi:10.1175/1520-0450(1990)029<0200:SPSSIL>2.0.CO;2.\n",
+    "\n",
+    "* Helmus JJ and SM Collis. 2016. “The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language.” Journal of Open Research Software, 4(1), doi:10.5334/jors.119."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": "3.11.4"
+  },
+  "nbdime-conflicts": {
+   "local_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "key": 0,
+         "op": "addrange",
+         "valuelist": [
+          "Python 3"
+         ]
+        },
+        {
+         "key": 0,
+         "length": 1,
+         "op": "removerange"
+        }
+       ],
+       "key": "display_name",
+       "op": "patch"
+      }
+     ],
+     "key": "kernelspec",
+     "op": "patch"
+    }
+   ],
+   "remote_diff": [
+    {
+     "diff": [
+      {
+       "diff": [
+        {
+         "key": 0,
+         "op": "addrange",
+         "valuelist": [
+          "Python3"
+         ]
+        },
+        {
+         "key": 0,
+         "length": 1,
+         "op": "removerange"
+        }
+       ],
+       "key": "display_name",
+       "op": "patch"
+      }
+     ],
+     "key": "kernelspec",
+     "op": "patch"
+    }
+   ]
+  },
+  "toc-autonumbering": false
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_toc.yml b/_toc.yml
index cde0cdfa..3fa57a72 100644
--- a/_toc.yml
+++ b/_toc.yml
@@ -6,6 +6,11 @@ parts:
     - file: getting-started.md
     - file: Templates/notebook-template
     - file: Templates/tutorial-schedule
+  - caption: Value-added Products
+    chapters:
+    - file: VAPs/README.md
+      sections:
+        - file: VAPs/squire/intro-to-squire.ipynb
   - caption: ARM/ASR PI Meeting 2023
     chapters:
     - file: Tutorials/arm-asr-pi-meeting-2023/README.md