{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using xesmf to efficiently regrid data to another resolution\n", "**Annette Hirsch, CLEX CMS**\n", "\n", "Often we want to compare to datasets but find that they are on different grids and first require to be interpolated to a common grid. \n", "\n", "There exist a number of different tools for doing this however in this post we'll talk about the regridding tools from xesmf." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First load the relevant packages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import xarray as xr\n", "import xesmf as xe\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Open the data. In this example, we will open a monthly surface temperature dataset from the ACCESS-1.3 CMIP5 model." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "url = 'http://dapds00.nci.org.au/thredds/dodsC/rr3/CMIP5/output1/CSIRO-BOM/ACCESS1-3/historical/mon/atmos/Amon/r1i1p1/latest/tas/tas_Amon_ACCESS1-3_historical_r1i1p1_185001-200512.nc'\n", "ds = xr.open_dataset(url)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:    (bnds: 2, lat: 145, lon: 192, time: 1872)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1850-01-16T12:00:00 ... 2005-12-16T12:00:00\n",
       "  * lat        (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n",
       "  * lon        (lon) float64 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
       "    height     float64 1.5\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) datetime64[ns] 1850-01-01 1850-02-01 ... 2006-01-01\n",
       "    lat_bnds   (lat, bnds) float64 -90.0 -89.38 -89.38 ... 89.38 89.38 90.0\n",
       "    lon_bnds   (lon, bnds) float64 -0.9375 0.9375 0.9375 ... 357.2 357.2 359.1\n",
       "    tas        (time, lat, lon) float32 ...\n",
       "Attributes: (12/29)\n",
       "    institution:                     CSIRO (Commonwealth Scientific and Indus...\n",
       "    institute_id:                    CSIRO-BOM\n",
       "    experiment_id:                   historical\n",
       "    source:                          ACCESS1-3 2011. Atmosphere: AGCM v1.0 (N...\n",
       "    model_id:                        ACCESS1.3\n",
       "    forcing:                         GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2,...\n",
       "    ...                              ...\n",
       "    title:                           ACCESS1-3 model output prepared for CMIP...\n",
       "    parent_experiment:               pre-industrial control\n",
       "    modeling_realm:                  atmos\n",
       "    realization:                     1\n",
       "    cmor_version:                    2.8.0\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time
" ], "text/plain": [ "\n", "Dimensions: (bnds: 2, lat: 145, lon: 192, time: 1872)\n", "Coordinates:\n", " * time (time) datetime64[ns] 1850-01-16T12:00:00 ... 2005-12-16T12:00:00\n", " * lat (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n", " * lon (lon) float64 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n", " height float64 ...\n", "Dimensions without coordinates: bnds\n", "Data variables:\n", " time_bnds (time, bnds) datetime64[ns] ...\n", " lat_bnds (lat, bnds) float64 ...\n", " lon_bnds (lon, bnds) float64 ...\n", " tas (time, lat, lon) float32 ...\n", "Attributes: (12/29)\n", " institution: CSIRO (Commonwealth Scientific and Indus...\n", " institute_id: CSIRO-BOM\n", " experiment_id: historical\n", " source: ACCESS1-3 2011. Atmosphere: AGCM v1.0 (N...\n", " model_id: ACCESS1.3\n", " forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2,...\n", " ... ...\n", " title: ACCESS1-3 model output prepared for CMIP...\n", " parent_experiment: pre-industrial control\n", " modeling_realm: atmos\n", " realization: 1\n", " cmor_version: 2.8.0\n", " DODS_EXTRA.Unlimited_Dimension: time" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For CMIP data the resolution may already be declared in the attributes of the file. If not, you can calculate this too:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'lat' ()>\n",
       "array(1.25)\n",
       "Coordinates:\n",
       "    height   float64 1.5
" ], "text/plain": [ "\n", "array(1.25)\n", "Coordinates:\n", " height float64 1.5" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(ds['lat'].max() - ds['lat'].min())/(ds['lat'].count()-1.)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'lon' ()>\n",
       "array(1.875)\n",
       "Coordinates:\n",
       "    height   float64 1.5
" ], "text/plain": [ "\n", "array(1.875)\n", "Coordinates:\n", " height float64 1.5" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(ds['lon'].max() - ds['lon'].min())/(ds['lon'].count()-1.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So the original resolution is 1.25 degrees latitude by 1.875 degrees longitude" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Say we want to regrid this to a 2.5 degree rectilinear grid, we can use the utils functions to define the new grid. Alternatively, if you want to regrid to the resolution of another dataset you can use a field directly from that dataset." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "ds_out = xe.util.grid_2d(-180.0, 180.0, 2.5, -90.0, 90.0, 2.5)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:  (x: 144, x_b: 145, y: 72, y_b: 73)\n",
       "Coordinates:\n",
       "    lon      (y, x) float64 -178.8 -176.2 -173.8 -171.2 ... 173.8 176.2 178.8\n",
       "    lat      (y, x) float64 -88.75 -88.75 -88.75 -88.75 ... 88.75 88.75 88.75\n",
       "    lon_b    (y_b, x_b) float64 -180.0 -177.5 -175.0 ... 175.0 177.5 180.0\n",
       "    lat_b    (y_b, x_b) float64 -90.0 -90.0 -90.0 -90.0 ... 90.0 90.0 90.0 90.0\n",
       "Dimensions without coordinates: x, x_b, y, y_b\n",
       "Data variables:\n",
       "    *empty*
" ], "text/plain": [ "\n", "Dimensions: (x: 144, x_b: 145, y: 72, y_b: 73)\n", "Coordinates:\n", " lon (y, x) float64 -178.8 -176.2 -173.8 -171.2 ... 173.8 176.2 178.8\n", " lat (y, x) float64 -88.75 -88.75 -88.75 -88.75 ... 88.75 88.75 88.75\n", " lon_b (y_b, x_b) float64 -180.0 -177.5 -175.0 ... 175.0 177.5 180.0\n", " lat_b (y_b, x_b) float64 -90.0 -90.0 -90.0 -90.0 ... 90.0 90.0 90.0 90.0\n", "Dimensions without coordinates: x, x_b, y, y_b\n", "Data variables:\n", " *empty*" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_out" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we build the regridder. In this first instance, the weights are not saved to netcdf. Note that there are a few options for the regridding method. As we are working with a global dataset, we use `periodic=True` so that we do not get gaps along the central longitude" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "regridder = xe.Regridder(ds, ds_out, 'bilinear', periodic=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "xESMF Regridder \n", "Regridding algorithm: bilinear \n", "Weight filename: bilinear_145x192_72x144_peri.nc \n", "Reuse pre-computed weights? False \n", "Input grid shape: (145, 192) \n", "Output grid shape: (72, 144) \n", "Periodic in longitude? True" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regridder" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As this file contains near-surface air temperature we'll define this separately." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'tas' (time: 1872, lat: 145, lon: 192)>\n",
       "[52116480 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1850-01-16T12:00:00 ... 2005-12-16T12:00:00\n",
       "  * lat      (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n",
       "  * lon      (lon) float64 0.0 1.875 3.75 5.625 7.5 ... 352.5 354.4 356.2 358.1\n",
       "    height   float64 1.5\n",
       "Attributes:\n",
       "    standard_name:     air_temperature\n",
       "    long_name:         Near-Surface Air Temperature\n",
       "    units:             K\n",
       "    cell_methods:      time: mean\n",
       "    cell_measures:     area: areacella\n",
       "    history:           2012-02-05T23:49:51Z altered by CMOR: Treated scalar d...\n",
       "    associated_files:  baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation...
" ], "text/plain": [ "\n", "[52116480 values with dtype=float32]\n", "Coordinates:\n", " * time (time) datetime64[ns] 1850-01-16T12:00:00 ... 2005-12-16T12:00:00\n", " * lat (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n", " * lon (lon) float64 0.0 1.875 3.75 5.625 7.5 ... 352.5 354.4 356.2 358.1\n", " height float64 1.5\n", "Attributes:\n", " standard_name: air_temperature\n", " long_name: Near-Surface Air Temperature\n", " units: K\n", " cell_methods: time: mean\n", " cell_measures: area: areacella\n", " history: 2012-02-05T23:49:51Z altered by CMOR: Treated scalar d...\n", " associated_files: baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation..." ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds['tas']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Apply the regridder to the data" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data3/hh5/public/apps/miniconda3/envs/analysis3-21.01/lib/python3.8/site-packages/xesmf/frontend.py:464: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n", " dr_out = xr.apply_ufunc(\n" ] } ], "source": [ "tas_25deg = regridder(ds['tas'])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 'tas' (time: 1872, y: 72, x: 144)>\n",
       "array([[[241.42548074, 241.46530151, 241.51575264, ..., 241.29264361,\n",
       "         241.3331604 , 241.37937428],\n",
       "        [249.29878775, 249.49865723, 249.70174442, ..., 248.53185656,\n",
       "         248.81828308, 249.07307976],\n",
       "        [258.90998768, 259.34362793, 259.62011489, ..., 257.00554583,\n",
       "         257.77041626, 258.39012844],\n",
       "        ...,\n",
       "        [240.0256449 , 240.15310669, 240.28757747, ..., 239.80319719,\n",
       "         239.92158508, 239.9827772 ],\n",
       "        [239.95589676, 239.94976807, 239.9454768 , ..., 239.91784213,\n",
       "         239.93392944, 239.96497476],\n",
       "        [239.91920767, 239.95617676, 239.98541397, ..., 239.76462219,\n",
       "         239.8212738 , 239.87550099]],\n",
       "\n",
       "       [[235.35084386, 235.43763733, 235.53132127, ..., 235.12648029,\n",
       "         235.19128418, 235.26917861],\n",
       "        [247.80141914, 248.36372375, 248.88822159, ..., 246.27575141,\n",
       "         246.73565674, 247.26041022],\n",
       "        [257.63035803, 257.96582031, 258.17488678, ..., 256.06124283,\n",
       "         256.6065979 , 257.11943182],\n",
       "...\n",
       "        [250.55788953, 250.56117249, 250.62096828, ..., 251.29435661,\n",
       "         250.8780365 , 250.63096473],\n",
       "        [252.6061043 , 252.39373779, 252.23463555, ..., 253.47047825,\n",
       "         253.12278748, 252.8264588 ],\n",
       "        [254.88175595, 254.8631897 , 254.86619789, ..., 255.01445768,\n",
       "         254.97032166, 254.92613684]],\n",
       "\n",
       "       [[246.75074031, 246.7674408 , 246.81818953, ..., 246.66889692,\n",
       "         246.68983459, 246.7283168 ],\n",
       "        [253.50811955, 253.73731995, 253.98962857, ..., 252.68888363,\n",
       "         252.96813965, 253.25622345],\n",
       "        [262.74184175, 263.19360352, 263.53787576, ..., 261.00175357,\n",
       "         261.69522095, 262.25131706],\n",
       "        ...,\n",
       "        [245.47192229, 245.0090332 , 244.5782707 , ..., 246.80896796,\n",
       "         246.32383728, 245.88781399],\n",
       "        [246.79688785, 246.40936279, 246.01888102, ..., 247.81054793,\n",
       "         247.51338196, 247.15659795],\n",
       "        [245.98595642, 245.89811707, 245.81729825, ..., 246.27256695,\n",
       "         246.18641663, 246.08195717]]])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1850-01-16T12:00:00 ... 2005-12-16T12:00:00\n",
       "    height   float64 1.5\n",
       "    lon      (y, x) float64 -178.8 -176.2 -173.8 -171.2 ... 173.8 176.2 178.8\n",
       "    lat      (y, x) float64 -88.75 -88.75 -88.75 -88.75 ... 88.75 88.75 88.75\n",
       "Dimensions without coordinates: y, x\n",
       "Attributes:\n",
       "    regrid_method:  bilinear
" ], "text/plain": [ "\n", "array([[[241.42548074, 241.46530151, 241.51575264, ..., 241.29264361,\n", " 241.3331604 , 241.37937428],\n", " [249.29878775, 249.49865723, 249.70174442, ..., 248.53185656,\n", " 248.81828308, 249.07307976],\n", " [258.90998768, 259.34362793, 259.62011489, ..., 257.00554583,\n", " 257.77041626, 258.39012844],\n", " ...,\n", " [240.0256449 , 240.15310669, 240.28757747, ..., 239.80319719,\n", " 239.92158508, 239.9827772 ],\n", " [239.95589676, 239.94976807, 239.9454768 , ..., 239.91784213,\n", " 239.93392944, 239.96497476],\n", " [239.91920767, 239.95617676, 239.98541397, ..., 239.76462219,\n", " 239.8212738 , 239.87550099]],\n", "\n", " [[235.35084386, 235.43763733, 235.53132127, ..., 235.12648029,\n", " 235.19128418, 235.26917861],\n", " [247.80141914, 248.36372375, 248.88822159, ..., 246.27575141,\n", " 246.73565674, 247.26041022],\n", " [257.63035803, 257.96582031, 258.17488678, ..., 256.06124283,\n", " 256.6065979 , 257.11943182],\n", "...\n", " [250.55788953, 250.56117249, 250.62096828, ..., 251.29435661,\n", " 250.8780365 , 250.63096473],\n", " [252.6061043 , 252.39373779, 252.23463555, ..., 253.47047825,\n", " 253.12278748, 252.8264588 ],\n", " [254.88175595, 254.8631897 , 254.86619789, ..., 255.01445768,\n", " 254.97032166, 254.92613684]],\n", "\n", " [[246.75074031, 246.7674408 , 246.81818953, ..., 246.66889692,\n", " 246.68983459, 246.7283168 ],\n", " [253.50811955, 253.73731995, 253.98962857, ..., 252.68888363,\n", " 252.96813965, 253.25622345],\n", " [262.74184175, 263.19360352, 263.53787576, ..., 261.00175357,\n", " 261.69522095, 262.25131706],\n", " ...,\n", " [245.47192229, 245.0090332 , 244.5782707 , ..., 246.80896796,\n", " 246.32383728, 245.88781399],\n", " [246.79688785, 246.40936279, 246.01888102, ..., 247.81054793,\n", " 247.51338196, 247.15659795],\n", " [245.98595642, 245.89811707, 245.81729825, ..., 246.27256695,\n", " 246.18641663, 246.08195717]]])\n", "Coordinates:\n", " * time (time) datetime64[ns] 1850-01-16T12:00:00 ... 2005-12-16T12:00:00\n", " height float64 1.5\n", " lon (y, x) float64 -178.8 -176.2 -173.8 -171.2 ... 173.8 176.2 178.8\n", " lat (y, x) float64 -88.75 -88.75 -88.75 -88.75 ... 88.75 88.75 88.75\n", "Dimensions without coordinates: y, x\n", "Attributes:\n", " regrid_method: bilinear" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tas_25deg" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comparing the dimensions we have:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "| | time | lat | lon |\n", "|:---:|:---:|:---:|:---:|\n", "| ds['tas'] | 1872 | 145 | 192 |\n", "| tas_25deg | 1872 | 72 | 144" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ds['tas'].isel(time=0).plot()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tas_25deg.isel(time=0).plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To save the weights for later use, you'll need to specify the filename to give to the weights file when you build the regridder for the first time:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "regridder_save_weights = xe.Regridder(ds,ds_out,'bilinear',periodic=True,filename='bilinear_145x192_180x360_peri.nc')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "xESMF Regridder \n", "Regridding algorithm: bilinear \n", "Weight filename: bilinear_145x192_180x360_peri.nc \n", "Reuse pre-computed weights? False \n", "Input grid shape: (145, 192) \n", "Output grid shape: (72, 144) \n", "Periodic in longitude? True" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regridder_save_weights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This will save a file called bilinear_145x192_180x360_peri.nc into the directory you are running this notebook from." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To build the regridder using existing weights:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "regridder_reuse_weights = xe.Regridder(ds,ds_out,'bilinear',periodic=True,reuse_weights=True,filename='bilinear_145x192_180x360_peri.nc')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "xESMF Regridder \n", "Regridding algorithm: bilinear \n", "Weight filename: bilinear_145x192_180x360_peri.nc \n", "Reuse pre-computed weights? True \n", "Input grid shape: (145, 192) \n", "Output grid shape: (72, 144) \n", "Periodic in longitude? True" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regridder_reuse_weights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Regridder will also work for 2D lat/lon that is common to curvilinear grids. In this case you'll need to create a new xr.Dataset containing the 2D lat/lon values from the grid you want to interpolate to. Often this can be done by copying the lat/lon from an existing file." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to learn more about the different options available within xesmf please check out: https://xesmf.readthedocs.io/en/latest/" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 4 }