{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Benchmarking nearest-neighbor regridding\n", "\n", "In this notebook we compare the results of three nearest-neighbor regridding methods:\n", "- The CDO (Climate Data Operators) command line program.\n", "- The xESMF xarray extension (based on the Earth System Modelling Framework).\n", "- xarray's `interp` method, wrapped as an accessor by `xarray-regrid`.\n", "\n", "The data to resample is the ERA5 monthly dataset, for the dewpoint temperature.\n", "\n", "We start with importing the required modules, starting up Dask and loading in the data:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from time import time\n", "import dask.distributed\n", "import xarray_regrid # Importing this will make Dataset.regrid accessible.\n", "import xarray as xr\n", "from xarray_regrid import Grid\n", "import xesmf as xe\n", "\n", "client = dask.distributed.Client()\n", "\n", "ds = xr.open_dataset(\n", " \"data/era5_2m_dewpoint_temperature_2000_monthly.nc\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both `xarray-regrid` and `xESMF` require a dataset to resample to. For this you can also use an already existing dataset with the desired grid.\n", "\n", "Here we use the `Grid` dataclass from `xarray-regrid` to create the dataset." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 6kB\n",
       "Dimensions:    (latitude: 265, longitude: 530)\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 2kB 45.0 45.17 45.34 ... 89.54 89.71 89.88\n",
       "  * longitude  (longitude) float64 4kB 90.0 90.17 90.34 ... 179.6 179.8 179.9\n",
       "Data variables:\n",
       "    *empty*
" ], "text/plain": [ " Size: 6kB\n", "Dimensions: (latitude: 265, longitude: 530)\n", "Coordinates:\n", " * latitude (latitude) float64 2kB 45.0 45.17 45.34 ... 89.54 89.71 89.88\n", " * longitude (longitude) float64 4kB 90.0 90.17 90.34 ... 179.6 179.8 179.9\n", "Data variables:\n", " *empty*" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_grid = Grid(\n", " north=90,\n", " east=180,\n", " south=45,\n", " west=90,\n", " resolution_lat=0.17,\n", " resolution_lon=0.17,\n", ")\n", "target_dataset = new_grid.create_regridding_dataset()\n", "target_dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## xarray-regrid\n", "\n", "With `xarray-regrid` you can access the regridding utilities by using `Dataset.regrid`.\n", "\n", "To make the comparison fair the lazy dataset is computed, to actually have the regridding performed." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Elapsed time: 0.208 seconds\n" ] } ], "source": [ "t0 = time()\n", "\n", "data_regrid = ds.regrid.nearest(target_dataset)\n", "data_regrid = data_regrid.compute()\n", "\n", "print(f\"Elapsed time: {time() - t0:.3f} seconds\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CDO\n", "\n", "The CDO data was regridded using the terminal.\n", "\n", "To ensure that the regridding is most accurate, the dataset was first converted to 64-bit floats:\n", " - `cdo -b 64 -copy era5_2m_dewpoint_temperature_2000_monthly.nc era5_2m_dewpoint_temperature_2000_monthly_64b.nc`\n", " - `cdo -b 64 -copy new_grid.nc new_grid_64b.nc`\n", "\n", "Next, the data can be regridded:\n", " - `cdo remapnn,new_grid_64b.nc era5_2m_dewpoint_temperature_2000_monthly_64b.nc cdo_nearest_64b.nc`\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "data_cdo = xr.open_dataset(\"data/cdo_nearest_64b.nc\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## xESMF\n", "\n", "For xESMF the regridding weights are first computed (`xe.Regridder`). \n", "\n", "After that, the regridder can be used to regrid the dataset:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Elapsed time: 5.300 seconds\n" ] } ], "source": [ "t0 = time()\n", "\n", "regridder = xe.Regridder(ds, target_dataset, \"nearest_s2d\")\n", "data_esmf: xr.Dataset = regridder(ds, keep_attrs=True)\n", "\n", "print(f\"Elapsed time: {time() - t0:.3f} seconds\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparison\n", "\n", "Now the data has been regridded using all three available methods, we can compare the results.\n", "\n", "The following code block computes the relative error `(a-b)/a` between the three datasets. All of them are identical." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from benchmark_utils import plot_comparison\n", "\n", "plot_comparison(\n", " data_regrid, data_esmf, data_cdo, vmin=-0.5e-10, vmax=0.5e-10, varname=\"d2m\"\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.12.0" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }