correct_projection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "99c2d966-fa99-4418-ba55-61b99c9fdc9e",
   "metadata": {},
   "source": [
    "The original way I had of handling the HRRR projection turned out to be imprecise, with up to almost 8km of error in the projection corners. Luckily, the GRIB files have the correct projection information and that works––mostly. Unfortunately, the projection created doesn't seem to work with matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "21bb49ab-475e-4495-8efb-68b255e3f98b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can get these with pygrib from any of the HRRR CONUS grib files:\n",
    "#     import pygrib\n",
    "#     grib_data = pygrib.open(grib_loc)\n",
    "#     proj_params = grib_data[1].projparams\n",
    "\n",
    "\n",
    "proj_params = {'a': 6371229,\n",
    "               'b': 6371229,\n",
    "               'proj': 'lcc',\n",
    "               'lon_0': 262.5,\n",
    "               'lat_0': 38.5,\n",
    "               'lat_1': 38.5,\n",
    "               'lat_2': 38.5}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f26b925a-505a-41d9-865c-cf3ae8829863",
   "metadata": {},
   "source": [
    "I had to update to the newest version of cartopy (0.20.1), but it does have support to directly create a CRS projection from these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b6447dd-529f-4db1-a254-db1f74639110",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cartopy.crs as ccrs\n",
    "projection = ccrs.CRS(proj_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef05260e-665a-434d-b346-0408cdb6ff5c",
   "metadata": {},
   "source": [
    "Let's try this out with an example HRRR Zarr:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c5339c28-2911-4c93-b610-9b442fdf1531",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The temperature at SLC International Airport on July 5 at 19z was 89.8 degree_Fahrenheit\n"
     ]
    }
   ],
   "source": [
    "import s3fs\n",
    "import xarray as xr\n",
    "from datetime import datetime\n",
    "from metpy.units import units\n",
    "\n",
    "def load_example_data():\n",
    "    reference_time = datetime(2021, 7, 5, 19)\n",
    "    var_name = \"TMP\"\n",
    "    var_level = \"surface\"\n",
    "\n",
    "    fs = s3fs.S3FileSystem(anon=True)\n",
    "    root_url = reference_time.strftime(\"s3://hrrrzarr/sfc/%Y%m%d/%Y%m%d_%Hz_anl.zarr\")\n",
    "\n",
    "    s3_lookups = [s3fs.S3Map(url, s3=fs) for url in [f\"{root_url}/{var_level}/{var_name}\", \n",
    "                                                     f\"{root_url}/{var_level}/{var_name}/{var_level}\"]]\n",
    "\n",
    "    return xr.open_mfdataset(s3_lookups, engine=\"zarr\")\n",
    "\n",
    "surface_temp = load_example_data()\n",
    "\n",
    "# SLC international airport\n",
    "airport_lat, airport_lon = 40.47, -111.59\n",
    "x, y = projection.transform_point(airport_lon, airport_lat, ccrs.PlateCarree())\n",
    "temperature = surface_temp.sel(projection_x_coordinate=x, \n",
    "                               projection_y_coordinate=y, method=\"nearest\").TMP.values\n",
    "\n",
    "temperature = (temperature * units.kelvin).to(units.fahrenheit)\n",
    "print(f\"The temperature at SLC International Airport on July 5 at 19z was {temperature:.1f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6a96c3d-d61b-47d2-a11b-2a9d335fc8be",
   "metadata": {},
   "source": [
    "We can also create a map of the data. Since the projection object doesn't work directly with matplotlib, first we generate the latitude and longitude values using metpy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "960cadab-5fa8-4880-8ba2-4076ac41efe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import metpy\n",
    "\n",
    "def assign_coordinates(ds):\n",
    "    ds = ds.rename(projection_x_coordinate=\"x\", projection_y_coordinate=\"y\")\n",
    "    ds = ds.metpy.assign_crs(projection.to_cf())\n",
    "    ds = ds.metpy.assign_latitude_longitude()\n",
    "    return ds\n",
    "\n",
    "surface_temp = assign_coordinates(surface_temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e68a4ae-8fe9-4d86-a2f9-964c3b1c4871",
   "metadata": {},
   "source": [
    "Now all we have to do is map this from the (latitude and longitude) PlateCarree projection!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b0ee7bc6-2d3e-45bf-9f54-676643830c57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.46 s, sys: 113 ms, total: 1.57 s\n",
      "Wall time: 2.52 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.contour.GeoContourSet at 0x16ca237c0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "plt.contourf(surface_temp.longitude, surface_temp.latitude, surface_temp.TMP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ba9258c-d161-4d10-9ed1-6cf49375f2d2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "gefs",
   "language": "python",
   "name": "gefs"
  },
  "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": 5
}