{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparison of six regridding algorithms\n", "\n", "xESMF exposes five different regridding algorithms from the ESMF library:\n", "\n", "- `bilinear`: `ESMF.RegridMethod.BILINEAR`\n", "- `conservative`: `ESMF.RegridMethod.CONSERVE`\n", "- `conservative_normed`: `ESMF.RegridMethod.CONSERVE`\n", "- `patch`: `ESMF.RegridMethod.PATCH`\n", "- `nearest_s2d`: `ESMF.RegridMethod.NEAREST_STOD`\n", "- `nearest_d2s`: `ESMF.RegridMethod.NEAREST_DTOS`\n", "\n", "where `conservative_normed` is just the `conservative` method with the\n", "normalization set to `ESMF.NormType.FRACAREA` instead of the default\n", "`norm_type=ESMF.NormType.DSTAREA`.\n", "\n", "This notebook demonstrates how these algorithms behave in different situations.\n", "\n", "## Notes\n", "\n", "- `bilinear` and `conservative` should be the most commonly used methods. They\n", " are both monotonic (i.e. will not create new maximum/minimum).\n", "- Nearest neighbour methods, either source to destination (s2d) or destination\n", " to source (d2s), could be useful in special cases. Keep in mind that d2s is\n", " highly non-monotonic.\n", "- Patch is ESMF's unique method, producing highly smooth results but quite slow.\n", "- From the ESMF documentation:\n", "\n", " > The weight $w_{ij}$ for a particular source cell $i$ and destination cell\n", " > $j$ are calculated as $w_{ij}=f_{ij} * A_{si}/A_{dj}$. In this equation\n", " > $f_{ij}$ is the fraction of the source cell $i$ contributing to destination\n", " > cell $j$, and $A_{si}$ and $A_{dj}$ are the areas of the source and\n", " > destination cells.\n", "\n", " For `conservative_normed`,\n", "\n", " > ... then the weights are further divided by the destination fraction. In\n", " > other words, in that case $w_{ij}=f_{ij} * A_{si}/(A_{dj}*D_j)$ where $D_j$\n", " > is fraction of the destination cell that intersects the unmasked source\n", " > grid.\n", "\n", "Detailed explanations are available on\n", "[ESMPy documentation](http://www.earthsystemmodeling.org/esmf_releases/last_built/esmpy_doc/html/api.html#regridding).\n", "\n", "## Preparation\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs\n", "import numpy as np\n", "import xarray as xr\n", "import xesmf as xe\n", "\n", "method_list = [\n", " \"bilinear\",\n", " \"conservative\",\n", " \"conservative_normed\",\n", " \"nearest_s2d\",\n", " \"nearest_d2s\",\n", " \"patch\",\n", "]" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "ds_in = xe.util.grid_global(20, 15) # input grid\n", "ds_fine = xe.util.grid_global(4, 4) # high-resolution target grid\n", "ds_coarse = xe.util.grid_global(30, 20) # low-resolution target grid" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a wave field that is widely used in regridding benchmarks.\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "Dimensions:  (y: 12, x: 18, y_b: 13, x_b: 19)\n",
       "Coordinates:\n",
       "    lon      (y, x) float64 -170.0 -150.0 -130.0 -110.0 ... 130.0 150.0 170.0\n",
       "    lat      (y, x) float64 -82.5 -82.5 -82.5 -82.5 ... 82.5 82.5 82.5 82.5\n",
       "    lon_b    (y_b, x_b) int64 -180 -160 -140 -120 -100 ... 100 120 140 160 180\n",
       "    lat_b    (y_b, x_b) int64 -90 -90 -90 -90 -90 -90 -90 ... 90 90 90 90 90 90\n",
       "Dimensions without coordinates: y, x, y_b, x_b\n",
       "Data variables:\n",
       "    data     (y, x) float64 2.016 2.009 1.997 1.987 ... 1.987 1.997 2.009 2.016
" ], "text/plain": [ "\n", "Dimensions: (y: 12, x: 18, y_b: 13, x_b: 19)\n", "Coordinates:\n", " lon (y, x) float64 -170.0 -150.0 -130.0 -110.0 ... 130.0 150.0 170.0\n", " lat (y, x) float64 -82.5 -82.5 -82.5 -82.5 ... 82.5 82.5 82.5 82.5\n", " lon_b (y_b, x_b) int64 -180 -160 -140 -120 -100 ... 100 120 140 160 180\n", " lat_b (y_b, x_b) int64 -90 -90 -90 -90 -90 -90 -90 ... 90 90 90 90 90 90\n", "Dimensions without coordinates: y, x, y_b, x_b\n", "Data variables:\n", " data (y, x) float64 2.016 2.009 1.997 1.987 ... 1.987 1.997 2.009 2.016" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_in[\"data\"] = xe.data.wave_smooth(ds_in[\"lon\"], ds_in[\"lat\"])\n", "ds_in" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ds_in[\"data\"].plot()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def regrid(ds_in, ds_out, dr_in, method):\n", " \"\"\"Convenience function for one-time regridding\"\"\"\n", " regridder = xe.Regridder(ds_in, ds_out, method, periodic=True)\n", " dr_out = regridder(dr_in)\n", " return dr_out" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When dealing with global grids, we need to set `periodic=True`, otherwise data\n", "along the meridian line will be missing.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Increasing resolution\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bilinear\n", "CPU times: user 386 ms, sys: 24.9 ms, total: 410 ms\n", "Wall time: 408 ms\n", "\n", "conservative\n", "CPU times: user 66.8 ms, sys: 3.79 ms, total: 70.6 ms\n", "Wall time: 70.4 ms\n", "\n", "conservative_normed\n", "CPU times: user 84.6 ms, sys: 266 µs, total: 84.9 ms\n", "Wall time: 84.6 ms\n", "\n", "nearest_s2d\n", "CPU times: user 29.6 ms, sys: 0 ns, total: 29.6 ms\n", "Wall time: 29.4 ms\n", "\n", "nearest_d2s\n", "CPU times: user 8.44 ms, sys: 0 ns, total: 8.44 ms\n", "Wall time: 8.22 ms\n", "\n", "patch\n", "CPU times: user 420 ms, sys: 12.3 ms, total: 432 ms\n", "Wall time: 431 ms\n", "\n" ] } ], "source": [ "for method in method_list:\n", " print(method)\n", " %time ds_fine[method] = regrid(ds_in, ds_fine, ds_in['data'], method)\n", " print('')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nearest neighbour algorithms are very fast while the patch method is quite slow.\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(3, 2, figsize=[8, 8])\n", "\n", "for i, method in enumerate(method_list):\n", " ax = axes.flatten()[i]\n", " ds_fine[method].plot.pcolormesh(ax=ax)\n", " ax.set_title(method, fontsize=15)\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When regridding from low-resolution to high-resolution, `bilinear` and `patch`\n", "will produce smooth results, while `conservative` and `nearest_s2d` will\n", "preserve the original coarse grid structure (although the data is now defined on\n", "a finer grid.).\n", "\n", "`nearest_d2s` is quite different from others: One source point can be mapped to\n", "**only one destination point**. Because we have far less source points (on a\n", "low-resolution grid) than destination points (on a high-resolution grid), most\n", "destination points cannot receive any data so they just have zero values. Only\n", "the destination points that are closest to source points can receive data.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Decreasing resolution\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "for method in method_list:\n", " ds_coarse[method] = regrid(ds_in, ds_coarse, ds_in[\"data\"], method)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(3, 2, figsize=[8, 8])\n", "\n", "for i, method in enumerate(method_list):\n", " ax = axes.flatten()[i]\n", " ds_coarse[method].plot.pcolormesh(ax=ax)\n", " ax.set_title(method, fontsize=15)\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When regridding from high-resolution to low-resolution, all methods except\n", "`nearest_d2s` produce similar results here. But that's largely because the input\n", "data is smooth. For real-world data, it is generally recommended to use\n", "`conservative` for decreasing the resolution, because it takes average over\n", "small source grid boxes, while `bilinear` and `nearest_s2d` effectively throw\n", "away most of source grid boxes.\n", "\n", "`nearest_d2s` is again different: **Every** source point **has to be** mapped to\n", "a destination point. Because we have far more source points (on a\n", "high-resolution grid) than destination points (on a low-resolution grid), a\n", "single destination point will receive data from multiple source points, which\n", "can accumulate to a large value (notice the colorbar range).\n" ] } ], "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": "3.9.13" }, "toc": { "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "toc_cell": false, "toc_position": {}, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }