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added docs notebook
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@larsbuntemeyer
larsbuntemeyer committed Jul 17, 2023
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Working with REMO domains"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The domain module should give some tools to work with preconfigured or user defined domains. Domains are defined as xarray datasets that will contain dimensions and coodinates according to CF-conventions. The pyremo domain module actually uses the [py-cordex](https://py-cordex.readthedocs.io/en/latest/domains.html) domains module in the background just with another set of tables.\n",
"\n",
"**NOTE**: Please be aware that a remo domain is usually a little larger than the “official” cordex domain according to the [archive specification](https://is-enes-data.github.io/cordex_archive_specifications.pdf) since the regional model usually accounts for a “buffer” zone where the lateral boundary conditions are nudged."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Working with domain information"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pyremo as pr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The domain module contains some useful functions to work with cordex meta data, e.g., you can get some domain grid information using"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pr.domain_info(\"EUR-11\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All available cordex domains are in this table:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pr.domains.table"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## `EUR-11` example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The heart of the module are some functions that create a dataset from the grid information, e.g."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"eur11 = pr.remo_domain(\"EUR-11\", dummy=\"topo\")\n",
"eur11"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `dummy='topo'` argument means, we want a dummy variable in the dataset to see how the domain looks like. For the dummy topography, we use the `cdo topo` operator in the background. So maybe you have to install `python-cdo`, e.g., `conda install -c conda-forge python-cdo`. Working with xarray datasets means, that we can use all the nice functions of xarray including plotting, e.g.,"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"eur11.topo.plot(cmap=\"terrain\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"eur11.topo.plot(x=\"lon\", y=\"lat\", cmap=\"terrain\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's define a slightly more sophisticated plotting function that uses cartopy for the right [projection](https://scitools.org.uk/cartopy/docs/latest/tutorials/understanding_transform.html) with a rotated pole:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def plot(da, pole, vmin=None, vmax=None, borders=True, title=\"\"):\n",
" \"\"\"plot a domain using the right projection with cartopy\"\"\"\n",
" %matplotlib inline\n",
" import cartopy.crs as ccrs\n",
" import cartopy.feature as cf\n",
" import matplotlib.pyplot as plt\n",
"\n",
" plt.figure(figsize=(20, 10))\n",
" projection = ccrs.PlateCarree()\n",
" transform = ccrs.RotatedPole(pole_latitude=pole[1], pole_longitude=pole[0])\n",
" # ax = plt.axes(projection=projection)\n",
" ax = plt.axes(projection=transform)\n",
" # ax.set_extent([ds_sub.rlon.min(), ds_sub.rlon.max(), ds_sub.rlat.min(), ds_sub.rlat.max()], crs=transform)\n",
" ax.gridlines(\n",
" draw_labels=True,\n",
" linewidth=0.5,\n",
" color=\"gray\",\n",
" xlocs=range(-180, 180, 10),\n",
" ylocs=range(-90, 90, 5),\n",
" )\n",
" da.plot(\n",
" ax=ax,\n",
" cmap=\"terrain\",\n",
" transform=transform,\n",
" vmin=vmin,\n",
" vmax=vmax,\n",
" x=\"rlon\",\n",
" y=\"rlat\",\n",
" )\n",
" ax.coastlines(resolution=\"50m\", color=\"black\", linewidth=1)\n",
" if borders:\n",
" ax.add_feature(cf.BORDERS)\n",
" ax.set_title(\"\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pole_eur = (\n",
" eur11.rotated_latitude_longitude.grid_north_pole_longitude,\n",
" eur11.rotated_latitude_longitude.grid_north_pole_latitude,\n",
")\n",
"pole_eur"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot(eur11.topo, pole_eur)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## User defined domain"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The domains are created using the `create_dataset` function from the [`py-cordex`](https://py-cordex.readthedocs.io) package, e.g.:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from cordex import create_dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's create the EUR-11 domain manually from the numbers in the table:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"eur11_user = create_dataset(\n",
" nlon=433,\n",
" nlat=433,\n",
" dlon=0.11,\n",
" dlat=0.11,\n",
" ll_lon=-28.925,\n",
" ll_lat=-23.925,\n",
" pollon=-162.00,\n",
" pollat=39.25,\n",
" dummy=\"topo\",\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can check that this gives the same result as our preconfigured domain."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"eur11_user.equals(eur11)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can now use the `create_dataset` function to create any domain as an xarray dataset."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"afr11 = pr.remo_domain(\"AFR-11\", dummy=\"topo\")\n",
"afr11"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pole_afr = (\n",
" afr11.rotated_latitude_longitude.grid_north_pole_longitude,\n",
" afr11.rotated_latitude_longitude.grid_north_pole_latitude,\n",
")\n",
"pole_afr"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot(afr11.topo, pole_afr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cropping the REMO domain"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes it might be neccessary to crop the REMO data to the official CORDEX grid size, e.g., for cmorization. This can now easily be done like this:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from cordex import cordex_domain\n",
"\n",
"eur11_cordex = cordex_domain(\"EUR-11\", dummy=\"topo\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# crop = eur11.sel(rlon=slice(eur11_cordex.rlon.min(), eur11_cordex.rlon.max()), rlat=slice(eur11_cordex.rlat.min(), eur11_cordex.rlat.max()))\n",
"crop = eur11.sel(rlon=eur11_cordex.rlon, rlat=eur11_cordex.rlat, method=\"nearest\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot(crop.topo, pole_eur)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"crop"
]
}
],
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