Rescale era5 wind speed with GWA3.1

scale_wind_data/scaling_script.ipynb

@@ -0,0 +1,176 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import atlite\n",
+    "import rioxarray\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.axes_grid1 import AxesGrid\n",
+    "import rasterio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def shiftedColorMap(cmap, start=0, midpoint=0.5, stop=1.0, name=\"shiftedcmap\"):\n",
+    "    \"\"\"\n",
+    "    Function to offset the \"center\" of a colormap. Useful for\n",
+    "    data with a negative min and positive max and you want the\n",
+    "    middle of the colormap's dynamic range to be at zero.\n",
+    "\n",
+    "    Input\n",
+    "    -----\n",
+    "      cmap : The matplotlib colormap to be altered\n",
+    "      start : Offset from lowest point in the colormap's range.\n",
+    "          Defaults to 0.0 (no lower offset). Should be between\n",
+    "          0.0 and `midpoint`.\n",
+    "      midpoint : The new center of the colormap. Defaults to\n",
+    "          0.5 (no shift). Should be between 0.0 and 1.0. In\n",
+    "          general, this should be  1 - vmax / (vmax + abs(vmin))\n",
+    "          For example if your data range from -15.0 to +5.0 and\n",
+    "          you want the center of the colormap at 0.0, `midpoint`\n",
+    "          should be set to  1 - 5/(5 + 15)) or 0.75\n",
+    "      stop : Offset from highest point in the colormap's range.\n",
+    "          Defaults to 1.0 (no upper offset). Should be between\n",
+    "          `midpoint` and 1.0.\n",
+    "    \"\"\"\n",
+    "    cdict = {\"red\": [], \"green\": [], \"blue\": [], \"alpha\": []}\n",
+    "\n",
+    "    # regular index to compute the colors\n",
+    "    reg_index = np.linspace(start, stop, 257)\n",
+    "\n",
+    "    # shifted index to match the data\n",
+    "    shift_index = np.hstack(\n",
+    "        [\n",
+    "            np.linspace(0.0, midpoint, 128, endpoint=False),\n",
+    "            np.linspace(midpoint, 1.0, 129, endpoint=True),\n",
+    "        ]\n",
+    "    )\n",
+    "\n",
+    "    for ri, si in zip(reg_index, shift_index):\n",
+    "        r, g, b, a = cmap(ri)\n",
+    "\n",
+    "        cdict[\"red\"].append((si, r, r))\n",
+    "        cdict[\"green\"].append((si, g, g))\n",
+    "        cdict[\"blue\"].append((si, b, b))\n",
+    "        cdict[\"alpha\"].append((si, a, a))\n",
+    "\n",
+    "    newcmap = matplotlib.colors.LinearSegmentedColormap(name, cdict)\n",
+    "\n",
+    "    return newcmap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load cutout and gwa raster. files need to be downloaded manually\n",
+    "cutout = atlite.Cutout(\"europe-2013-era5.nc\")\n",
+    "gwa_data = rioxarray.open_rasterio(\"gwa3_250_wind-speed_100m.tif\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use version 1.2.9 of rasterio for the working Resampling by mean\n",
+    "# downsamples the gwa data to match the raster of the cutout. The resampling is done with averaging the wind speed.\n",
+    "ds = gwa_data.sel(\n",
+    "    band=1, x=slice(*cutout.extent[[0, 1]]), y=slice(*cutout.extent[[3, 2]])\n",
+    ")\n",
+    "ds = ds.where(ds != -999)\n",
+    "ds = atlite.gis.regrid(\n",
+    "    ds, cutout.data.x, cutout.data.y, resampling=rasterio.warp.Resampling.average\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(1, 3, figsize=(20, 10))\n",
+    "\n",
+    "cutout.data.wnd100m.mean(\"time\").plot(ax=ax[0], vmin=0, vmax=15)\n",
+    "\n",
+    "ds.plot(ax=ax[1], vmin=0, vmax=15)\n",
+    "\n",
+    "newcmap = shiftedColorMap(cmap=matplotlib.cm.PiYG, start=0, stop=1, midpoint=0.5)\n",
+    "# gwa wind speed mean divided by cutout wind speed mean -> corresponds to the bias correction factor\n",
+    "(ds / cutout.data.wnd100m.mean(\"time\")).plot(ax=ax[2], cmap=newcmap, vmax=2, vmin=0)\n",
+    "\n",
+    "ax[0].set_title(\"era5\")\n",
+    "ax[1].set_title(\"gwa3.1\")\n",
+    "ax[2].set_title(\"ratio gwa3.1/era5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(ds / cutout.data.wnd100m.mean(\"time\")).mean().compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig.savefig(\"gwa_era5.pdf\", dpi=400, facecolor=\"white\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "",
+   "language": "python",
+   "name": ""
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