Another Supporting nb for https://discourse.jupyter.org/t/unable-to-show-the-print-output-of-my-notebook/25523/5?u=fomightez , see rendered at https://nbviewer.org/urls/gist.githubusercontent.com/fomightez/e38afa45324e26a6a67470ef109eb9b6/raw/2bfe0b927328fd9df840f18903ff53c4032b04eb/Vorticity%252520and%252520Divergence%252520Inversion.ipynb

Vorticity%20and%20Divergence%20Inversion.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a43ad10b-f6ad-418a-8da3-4b5a779f819a",
   "metadata": {},
   "source": [
    "#### Demo of trying Vorticity_Divergence_Inversion.ipynb via MyBinder.org-served session\n",
    "\n",
    "Went to here and clicked 'launch binder'.\n",
    "\n",
    "In the session that came up, I opened a terminal and ran `curl -OL https://raw.githubusercontent.com/evans36/miscellany/34782117b704838b4ab6b7b3a918b68560ea1053/Vorticity%20and%20Divergence%20Inversion.ipynb`\n",
    "\n",
    "In the file navigation panel on the left, I double clicked on the file `Vorticity%20and%20Divergence%20Inversion.ipynb` that was listed there now after running the curl command above.\n",
    "\n",
    "In the top of that notebook, I made a new cell and pasted in the following code that will install the necessary packages to run the first few cells and ran it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a736f51-2bab-432a-bd4d-4fa8ac2abb4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install cartopy\n",
    "%pip install boto3\n",
    "%pip install metpy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "278a2465",
   "metadata": {},
   "source": [
    "<h2>Vorticity and Divergence Inversion</h2>\n",
    "\n",
    "This Notebook demonstrates how to 'invert' relative-vorticity and divergence fields to obtain the horizontal winds uniquely associated with that field (i.e., the non-divergent and irrotational wind components, respectively) over a limited-area domain. \n",
    "\n",
    "This example uses a real-time GFS analysis to invert the analyzed 850-hPa relative vorticity and divergence associated with Hurricane Dora (2023) in the Central Pacific.\n",
    "\n",
    "<hr>\n",
    "\n",
    "We start by loading the required modules:\n",
    "<ul>\n",
    "    <li><b>boto3</b> to access GFS data hosted on Amazon Web Services' S3 NOAA GFS bucket. We also need to import a couple of modules from <b>botocore</b> to access the GFS bucket via boto3 without AWS credentials.</li>\n",
    "    <li><b>xarray</b> to open and parse the data. Note that an appropriate GRIB decoder such as cfgrib must be installed in order for xarray to be able to read GRIB-formatted data.</li>\n",
    "    <li><b>metpy</b> to compute the relative vorticity.</li>\n",
    "    <li><b>matplotlib</b> and <b>cartopy</b> to create maps from the data.</li>\n",
    "    <li><b>numpy</b> to help with various mathematical operations (e.g., evenly spaced lat/lon lines for maps).</li>\n",
    "    <li><b>os</b>, specifically the <i>path</i> function, to check to see if the desired GFS data already exist locally.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0aada0d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os.path\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import boto3\n",
    "import metpy.calc as mpcalc\n",
    "from botocore import UNSIGNED\n",
    "from botocore.config import Config\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.cm import get_cmap\n",
    "import matplotlib.ticker as mticker\n",
    "import cartopy.crs as crs\n",
    "from cartopy.feature import NaturalEarthFeature\n",
    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4521c80",
   "metadata": {},
   "source": [
    "We'll now download the desired GFS analysis - here being the 1200 UTC 9 August 2023 analysis. We use the 0.5° analysis to save computational time. This is just shy of 150 MB, so it may take a minute to download on slower connections. We use os.path to check to see if this file has already been downloaded so we can skip this step if needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f9dd5e96",
   "metadata": {},
   "outputs": [],
   "source": [
    "if(not os.path.isfile('gfs.t12z.pgrb2.0p50.f000')):\n",
    "    client = boto3.client('s3', config=Config(signature_version=UNSIGNED))\n",
    "    client.download_file('noaa-gfs-bdp-pds', 'gfs.20230809/12/atmos/gfs.t12z.pgrb2.0p50.f000', 'gfs.t12z.pgrb2.0p50.f000')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db32573f",
   "metadata": {},
   "source": [
    "Let's open the data into two arrays, one for the 850 hPa <i>u</i> wind component and one for the 850 hPa <i>v</i> wind component:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2fab255b",
   "metadata": {},
   "outputs": [],
   "source": [
    "u850 = xr.open_dataset('gfs.t12z.pgrb2.0p50.f000', engine='cfgrib', filter_by_keys={'typeOfLevel': 'isobaricInhPa', 'shortName': 'u', 'level': 850})\n",
    "u = u850.u\n",
    "v850 = xr.open_dataset('gfs.t12z.pgrb2.0p50.f000', engine='cfgrib', filter_by_keys={'typeOfLevel': 'isobaricInhPa', 'shortName': 'v', 'level': 850})\n",
    "v = v850.v"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cf6eb49",
   "metadata": {},
   "source": [
    "Now that we've opened our dataset, let's create a plot of 850 hPa relative vorticity over a limited region corresponding to Hurricane Dora."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a3dc2d75",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1115/3638860538.py:16: UserWarning: More than one time coordinate present for variable  \"u\".\n",
      "  vort850 = mpcalc.vorticity(u, v)\n",
      "/tmp/ipykernel_1115/3638860538.py:19: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
      "  plot = vort850.plot(levels=np.arange(-1.e-4, 1.e-4, 0.2e-5), cmap=get_cmap('PRGn'), transform=crs.PlateCarree(), cbar_kwargs={'label':'relative vorticity (x$10^{-5} s^{-1}$)', 'shrink': 0.98})\n",
      "/srv/conda/envs/notebook/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_cultural/ne_50m_admin_0_countries.zip\n",
      "  warnings.warn(f'Downloading: {url}', DownloadWarning)\n",
      "/srv/conda/envs/notebook/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip\n",
      "  warnings.warn(f'Downloading: {url}', DownloadWarning)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 3600x2700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure instance.\n",
    "fig = plt.figure(figsize=(12,9), dpi=300.)\n",
    "\n",
    "# Create a set of axes for the figure and set\n",
    "# its map projection to that of the input data.\n",
    "ax = plt.axes(projection=crs.PlateCarree())\n",
    "\n",
    "# Add country borders and coastlines.\n",
    "countries = NaturalEarthFeature(category=\"cultural\", scale=\"50m\",\n",
    "                                      facecolor=\"none\",\n",
    "                                      name=\"admin_0_countries\")\n",
    "ax.add_feature(countries, linewidth=.5, edgecolor=\"black\")\n",
    "ax.coastlines('50m', linewidth=0.8)\n",
    "\n",
    "# Compute the 850 hPa relative vorticity.\n",
    "vort850 = mpcalc.vorticity(u, v)\n",
    "\n",
    "# Plot the 850 hPa relative vorticity using xarray's plot functionality.\n",
    "plot = vort850.plot(levels=np.arange(-1.e-4, 1.e-4, 0.2e-5), cmap=get_cmap('PRGn'), transform=crs.PlateCarree(), cbar_kwargs={'label':'relative vorticity (x$10^{-5} s^{-1}$)', 'shrink': 0.98})\n",
    "\n",
    "# Set the map's extent to cover just Hurricane Dora.\n",
    "ax.set_extent([-180.,-150.,0.,20.],crs=crs.PlateCarree())\n",
    "\n",
    "# Add latitude/longitude gridlines.\n",
    "gridlines = ax.gridlines(color=\"grey\", linestyle=\"dotted\", draw_labels=True)\n",
    "gridlines.xlabels_top = False\n",
    "gridlines.ylabels_right = False\n",
    "gridlines.xlocator = mticker.FixedLocator(np.arange(-180.,149.,5.))\n",
    "gridlines.ylocator = mticker.FixedLocator(np.arange(0.,21.,5.))\n",
    "gridlines.xlabel_style = {'size':12, 'color':'black'}\n",
    "gridlines.ylabel_style = {'size':12, 'color':'black'}\n",
    "gridlines.xformatter = LONGITUDE_FORMATTER\n",
    "gridlines.yformatter = LATITUDE_FORMATTER\n",
    "\n",
    "# Add a plot title, then show the image.\n",
    "plt.title(\"GFS 0-h 850 hPa relative vorticity (x$10^{-5} s^{-1}$) at 1200 UTC 9 August 2023\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5df802f5",
   "metadata": {},
   "source": [
    "Let's also create a plot of 850 hPa divergence over this region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "138d28e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1115/1310903732.py:19: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
      "  plot = div850.plot(levels=np.arange(-1.e-4, 1.e-4, 0.2e-5), cmap=get_cmap('PRGn'), transform=crs.PlateCarree(), cbar_kwargs={'label':'relative vorticity (x$10^{-5} s^{-1}$)', 'shrink': 0.98})\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x2700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure instance.\n",
    "fig = plt.figure(figsize=(12,9), dpi=300.)\n",
    "\n",
    "# Create a set of axes for the figure and set\n",
    "# its map projection to that of the input data.\n",
    "ax = plt.axes(projection=crs.PlateCarree())\n",
    "\n",
    "# Add country borders and coastlines.\n",
    "countries = NaturalEarthFeature(category=\"cultural\", scale=\"50m\",\n",
    "                                      facecolor=\"none\",\n",
    "                                      name=\"admin_0_countries\")\n",
    "ax.add_feature(countries, linewidth=.5, edgecolor=\"black\")\n",
    "ax.coastlines('50m', linewidth=0.8)\n",
    "\n",
    "# Compute the 850 hPa divergence.\n",
    "div850 = mpcalc.divergence(u, v)\n",
    "\n",
    "# Plot the 850 hPa divergence using xarray's plot functionality.\n",
    "plot = div850.plot(levels=np.arange(-1.e-4, 1.e-4, 0.2e-5), cmap=get_cmap('PRGn'), transform=crs.PlateCarree(), cbar_kwargs={'label':'relative vorticity (x$10^{-5} s^{-1}$)', 'shrink': 0.98})\n",
    "\n",
    "# Set the map's extent to cover just Hurricane Dora.\n",
    "ax.set_extent([-180.,-150.,0.,20.],crs=crs.PlateCarree())\n",
    "\n",
    "# Add latitude/longitude gridlines.\n",
    "gridlines = ax.gridlines(color=\"grey\", linestyle=\"dotted\", draw_labels=True)\n",
    "gridlines.xlabels_top = False\n",
    "gridlines.ylabels_right = False\n",
    "gridlines.xlocator = mticker.FixedLocator(np.arange(-180.,149.,5.))\n",
    "gridlines.ylocator = mticker.FixedLocator(np.arange(0.,21.,5.))\n",
    "gridlines.xlabel_style = {'size':12, 'color':'black'}\n",
    "gridlines.ylabel_style = {'size':12, 'color':'black'}\n",
    "gridlines.xformatter = LONGITUDE_FORMATTER\n",
    "gridlines.yformatter = LATITUDE_FORMATTER\n",
    "\n",
    "# Add a plot title, then show the image.\n",
    "plt.title(\"GFS 0-h 850 hPa divergence (x$10^{-5} s^{-1}$) at 1200 UTC 9 August 2023\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91f0dbf8",
   "metadata": {},
   "source": [
    "Hurricane Dora was located at 11.4°N, 163.1°W at 1200 UTC 9 August 2023. The plots above shows that Dora is a fairly small hurricane, with cyclonic relative vorticity that can reliably be ascribed to Dora located between 5-13.5°N, 169-158°W. The divergence field is noisier, with unexpected strong divergence near Dora's center, albeit with convergence limited to a smaller area. Nevertheless, for consitency, we'll use the same box (5-13.5°N, 169-158°W) to invert the vorticity and divergence, with each field set to zero outside of this box."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f9534ebc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note the need to use 0->360° rather than -180->180° longitude in defining the mask.\n",
    "mask = ((vort850.latitude <= 13.5) & (vort850.latitude >= 5.0) & (vort850.longitude <= 202.) & (vort850.longitude >= 191.))\n",
    "vortmask = vort850.where(mask)\n",
    "vortmask = vortmask.fillna(0.0)\n",
    "divmask = div850.where(mask)\n",
    "divmas = div850.fillna(0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "183fa8e4",
   "metadata": {},
   "source": [
    "We now want to compute the non-divergent (i.e., that associated with rotation) and irrotational (i.e., that associated with divergence) wind components:\n",
    "<ul>\n",
    "    <li>The relative vorticity is equal to the Laplacian of the streamfunction on a horizontal surface. Solving this Poisson equation and using the relationship between the streamfunction and horizontal wind components, we can obtain expressions for the u- and v-components of the non-divergent wind in terms of the relative vorticity. The equations below follow Oertel and Schemm (2021, QJRMS), specifically their Eqns. (8) and (9).</li>\n",
    "    <li>The divergence is equal to the Laplacian of the velocity potential on a horizontal surface. Solving this Poission equation and using the relationship between the velocity potential and horizontal wind components, we can obtain expressions for the u- and v-components of the irrotational wind in temrs of the divergence. The equations below follow Oertel and Schemm (2021, QJRMS), specifically their Eqns. (10) and (11).</li>\n",
    "\n",
    "We start by computing the horizontal distance between grid points in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "97bff397",
   "metadata": {},
   "outputs": [],
   "source": [
    "dx, dy = mpcalc.lat_lon_grid_deltas(vortmask.longitude, vortmask.latitude)\n",
    "dx = np.abs(dx)\n",
    "dy = np.abs(dy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "521201c2",
   "metadata": {},
   "source": [
    "Next, we create variables to store the spatially varying non-divergent (upsi, vpsi) and irrotational (uchi, vchi) wind components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0b42064e",
   "metadata": {},
   "outputs": [],
   "source": [
    "upsi = xr.zeros_like(vortmask)\n",
    "vpsi = xr.zeros_like(vortmask)\n",
    "uchi = xr.zeros_like(divmask)\n",
    "vchi = xr.zeros_like(divmask)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b2d656d",
   "metadata": {},
   "source": [
    "We want to compute the contribution of the vorticity and divergence within our bounding box to the non-divergent and irrotational wind components, respectively, across a slightly larger area than our bounding box. To do so, we need to find the indices of our bounding box (corresponding to 5-13.5°N, 158-169°W, as above) and of the desired larger subset of the GFS grid. We want this to be a bit larger than the map extent above so that we can fully encompass Hawaii, so let's use 0-30°N, 140-180°W for this larger subset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "88737802",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_ll = list(vortmask.longitude.values).index(191.0)\n",
    "x_ur = list(vortmask.longitude.values).index(202.0)\n",
    "y_ll = list(vortmask.latitude.values).index(5.0)\n",
    "y_ur = list(vortmask.latitude.values).index(13.5)\n",
    "\n",
    "x_ll_subset = list(vortmask.longitude.values).index(180.0)\n",
    "x_ur_subset = list(vortmask.longitude.values).index(220.0)\n",
    "y_ll_subset = list(vortmask.latitude.values).index(0.0)\n",
    "y_ur_subset = list(vortmask.latitude.values).index(30.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab7723ff",
   "metadata": {},
   "source": [
    "Now that we have our bounding box, we can iteratively compute the non-divergent and irrotational wind components. Note that since y = 1 is the top latitude (90°N) rather than bottom latitude (90°S), the loop in latitude is top-down.\n",
    "\n",
    "<b>Note that the data are stored with latitude as the first dimension and longitude as the second dimension, thus the indexing below is cast in terms of j,i rather than the traditional i,j.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8f89822",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate over each model grid point (larger area).\n",
    "# i and j refer to x and y in the Oertel and Schemm (2021) equations.\n",
    "for i in range(x_ll_subset, x_ur_subset):\n",
    "    for j in range(y_ur_subset, y_ll_subset): \n",
    "\n",
    "# Computing the contribution to each other grid point (masked area).\n",
    "# x1 and y1 refer to x' and y' in the Oertel and Schemm (2021) equations.\n",
    "        for x1 in range(x_ll, x_ur):\n",
    "            for y1 in range(y_ur, y_ll):\n",
    "                outer_point = [i,j]\n",
    "                inner_point = [x1,y1]\n",
    "                \n",
    "                if inner_point != outer_point:\n",
    "                    \n",
    "                # Compute x-x', y-y', and r^2...\n",
    "                    xdiff = (i-x1)*dx[y1,x1].magnitude\n",
    "                    ydiff = (j-y1)*dy[y1,x1].magnitude\n",
    "                    rsq = (xdiff*xdiff) + (ydiff*ydiff)\n",
    "                \n",
    "                # Compute the non-divergent flow contribution.\n",
    "                    upsi[j,i] += vortmask[y1,x1].values * -1.0 * (ydiff / rsq) * dx[y1,x1].magnitude * dy[y1,x1].magnitude\n",
    "                    vpsi[j,i] += vortmask[y1,x1].values * (xdiff / rsq) * dx[y1,x1].magnitude * dy[y1,x1].magnitude\n",
    "                \n",
    "                # Compute the irrotational flow contribution.\n",
    "                    uchi[j,i] += divmask[y1,x1].values * (xdiff / rsq) * dx[y1,x1].magnitude * dy[y1,x1].magnitude\n",
    "                    vchi[j,i] += divmask[y1,x1].values * (ydiff / rsq) * dx[y1,x1].magnitude * dy[y1,x1].magnitude\n",
    "\n",
    "        # Once all points have been iterated over, scale by 1/2pi.\n",
    "        upsi[j,i] = (1/(2*np.pi)) * upsi[j,i]\n",
    "        vpsi[j,i] = (1/(2*np.pi)) * vpsi[j,i]\n",
    "        uchi[j,i] = (1/(2*np.pi)) * uchi[j,i]\n",
    "        vchi[j,i] = (1/(2*np.pi)) * vchi[j,i]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "295f41a8",
   "metadata": {},
   "source": [
    "Now that we have the non-divergent and irrotational wind components, let's create a few plots: the non-divergent wind magnitude, the irrotational wind magnitude, and the sum of the two!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a559277",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a figure instance.\n",
    "fig = plt.figure(figsize=(12,9), dpi=300.)\n",
    "\n",
    "# Create a set of axes for the figure and set\n",
    "# its map projection to that of the input data.\n",
    "ax = plt.axes(projection=crs.PlateCarree())\n",
    "\n",
    "# Add country borders and coastlines.\n",
    "countries = NaturalEarthFeature(category=\"cultural\", scale=\"50m\",\n",
    "                                      facecolor=\"none\",\n",
    "                                      name=\"admin_0_countries\")\n",
    "ax.add_feature(countries, linewidth=.5, edgecolor=\"black\")\n",
    "ax.coastlines('50m', linewidth=0.8)\n",
    "\n",
    "# Compute the magnitude of the non-divergent component of the 850 hPa wind.\n",
    "nd_spd = np.sqrt(upsi**2 + vpsi**2)\n",
    "\n",
    "# Plot this using xarray's plot functionality.\n",
    "plot = nd_spd.plot(levels=np.arange(0., 13., 1.), cmap=get_cmap('YlGnBu'), transform=crs.PlateCarree(), cbar_kwargs={'label':'non-divergent wind ($m s^{-1}$)', 'shrink': 0.98})\n",
    "\n",
    "# Set the map's extent to match that over which we computed the non-divergent wind.\n",
    "ax.set_extent([-180.,-140.,0.,30.],crs=crs.PlateCarree())\n",
    "\n",
    "# Add latitude/longitude gridlines.\n",
    "gridlines = ax.gridlines(color=\"grey\", linestyle=\"dotted\", draw_labels=True)\n",
    "gridlines.xlabels_top = False\n",
    "gridlines.ylabels_right = False\n",
    "gridlines.xlocator = mticker.FixedLocator(np.arange(-180.,139.,5.))\n",
    "gridlines.ylocator = mticker.FixedLocator(np.arange(0.,31.,5.))\n",
    "gridlines.xlabel_style = {'size':12, 'color':'black'}\n",
    "gridlines.ylabel_style = {'size':12, 'color':'black'}\n",
    "gridlines.xformatter = LONGITUDE_FORMATTER\n",
    "gridlines.yformatter = LATITUDE_FORMATTER\n",
    "\n",
    "# Add a plot title, then show the image.\n",
    "plt.title(\"GFS 0-h 850 hPa non-divergent wind magnitude ($m s^{-1}$) due to Dora at 1200 UTC 9 August 2023\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e4fe883",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a figure instance.\n",
    "fig = plt.figure(figsize=(12,9), dpi=300.)\n",
    "\n",
    "# Create a set of axes for the figure and set\n",
    "# its map projection to that of the input data.\n",
    "ax = plt.axes(projection=crs.PlateCarree())\n",
    "\n",
    "# Add country borders and coastlines.\n",
    "countries = NaturalEarthFeature(category=\"cultural\", scale=\"50m\",\n",
    "                                      facecolor=\"none\",\n",
    "                                      name=\"admin_0_countries\")\n",
    "ax.add_feature(countries, linewidth=.5, edgecolor=\"black\")\n",
    "ax.coastlines('50m', linewidth=0.8)\n",
    "\n",
    "# Compute the magnitude of the irrotational component of the 850 hPa wind.\n",
    "irr_spd = np.sqrt(uchi**2 + vchi**2)\n",
    "\n",
    "# Plot this using xarray's plot functionality.\n",
    "plot = irr_spd.plot(levels=np.arange(0., 13., 1.), cmap=get_cmap('YlGnBu'), transform=crs.PlateCarree(), cbar_kwargs={'label':'irrotational wind ($m s^{-1}$)', 'shrink': 0.98})\n",
    "\n",
    "# Set the map's extent to match that over which we computed the irrotational wind.\n",
    "ax.set_extent([-180.,-140.,0.,30.],crs=crs.PlateCarree())\n",
    "\n",
    "# Add latitude/longitude gridlines.\n",
    "gridlines = ax.gridlines(color=\"grey\", linestyle=\"dotted\", draw_labels=True)\n",
    "gridlines.xlabels_top = False\n",
    "gridlines.ylabels_right = False\n",
    "gridlines.xlocator = mticker.FixedLocator(np.arange(-180.,139.,5.))\n",
    "gridlines.ylocator = mticker.FixedLocator(np.arange(0.,31.,5.))\n",
    "gridlines.xlabel_style = {'size':12, 'color':'black'}\n",
    "gridlines.ylabel_style = {'size':12, 'color':'black'}\n",
    "gridlines.xformatter = LONGITUDE_FORMATTER\n",
    "gridlines.yformatter = LATITUDE_FORMATTER\n",
    "\n",
    "# Add a plot title, then show the image.\n",
    "plt.title(\"GFS 0-h 850 hPa irrotational wind magnitude ($m s^{-1}$) due to Dora at 1200 UTC 9 August 2023\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40763f79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a figure instance.\n",
    "fig = plt.figure(figsize=(12,9), dpi=300.)\n",
    "\n",
    "# Create a set of axes for the figure and set\n",
    "# its map projection to that of the input data.\n",
    "ax = plt.axes(projection=crs.PlateCarree())\n",
    "\n",
    "# Add country borders and coastlines.\n",
    "countries = NaturalEarthFeature(category=\"cultural\", scale=\"50m\",\n",
    "                                      facecolor=\"none\",\n",
    "                                      name=\"admin_0_countries\")\n",
    "ax.add_feature(countries, linewidth=.5, edgecolor=\"black\")\n",
    "ax.coastlines('50m', linewidth=0.8)\n",
    "\n",
    "# Add the non-divergent and irrotational wind magnitudes.\n",
    "tot_spd = nd_spd + irr_spd\n",
    "\n",
    "# Plot this using xarray's plot functionality.\n",
    "plot = tot_spd.plot(levels=np.arange(0., 16., 1.), cmap=get_cmap('YlGnBu'), transform=crs.PlateCarree(), cbar_kwargs={'label':'irrotational + non-divergent wind ($m s^{-1}$)', 'shrink': 0.98})\n",
    "\n",
    "# Set the map's extent to match that over which we computed the irrotational wind.\n",
    "ax.set_extent([-180.,-140.,0.,30.],crs=crs.PlateCarree())\n",
    "\n",
    "# Add latitude/longitude gridlines.\n",
    "gridlines = ax.gridlines(color=\"grey\", linestyle=\"dotted\", draw_labels=True)\n",
    "gridlines.xlabels_top = False\n",
    "gridlines.ylabels_right = False\n",
    "gridlines.xlocator = mticker.FixedLocator(np.arange(-180.,139.,5.))\n",
    "gridlines.ylocator = mticker.FixedLocator(np.arange(0.,31.,5.))\n",
    "gridlines.xlabel_style = {'size':12, 'color':'black'}\n",
    "gridlines.ylabel_style = {'size':12, 'color':'black'}\n",
    "gridlines.xformatter = LONGITUDE_FORMATTER\n",
    "gridlines.yformatter = LATITUDE_FORMATTER\n",
    "\n",
    "# Add a plot title, then show the image.\n",
    "plt.title(\"GFS 0-h 850 hPa irrotational + non-divergent wind magnitude ($m s^{-1}$)\\ndue to Dora at 1200 UTC 9 August 2023\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37b118a5-6a9d-47ed-be45-71a31c1501c0",
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "204dc058-5219-4e35-95a5-dc4fe6f830ca",
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "4a4761a8-b066-431a-9d8a-7a5b9331e558",
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "a304d654-77f5-4040-be09-df698153366e",
   "metadata": {},
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   "source": []
  }
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Despite what GitHub indicates above, the run notebook is rendered nicely HERE via nbviewer.