03_HRRR_Dashboard.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HRRR Dashboard\n",
    "The High Resolution Rapid Refresh (HRRR, pronouned \"her\") is the highest resolution (2.5km) weather forecast for the entire Continental USA.  Here we investigate the gridded HRRR forecast data products from [Unidata's THREDDS server](http://thredds.ucar.edu) and visualizing the data using the [holoviz](holoviz.org) tools. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'http://thredds.ucar.edu/thredds/dodsC/grib/NCEP/HRRR/CONUS_2p5km/Best'\n",
    "ds = xr.open_dataset(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find all the data variables that depend on time (and are not time `bounds`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_vars = []\n",
    "for var in ds.data_vars:\n",
    "    if len(ds[var].dims) > 0:\n",
    "        if 'time' in ds[var].dims[0] and not 'bounds' in var:\n",
    "            time_vars.append(var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the coordinate reference system (crs) using metpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import metpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_var = 'Temperature_height_above_ground'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds  = ds.metpy.parse_cf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "crs = ds[init_var].metpy.cartopy_crs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the [holoviz](https://holoviz.org) tools we need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cartopy import crs as ccrs\n",
    "import hvplot.xarray\n",
    "import holoviews as hv\n",
    "from geoviews import tile_sources as gvts\n",
    "import panel as pn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create widget for variable selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_select = pn.widgets.Select(name='HRRR Variables:', options=time_vars, \n",
    "                               value=init_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create widget for basemap selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_map_select = pn.widgets.Select(name='Basemap:', options=gvts.tile_sources, value=gvts.OSM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a color mesh plot in Lambert Conformal coordinates with `hvplot` and `cartopy`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The `plot` function below creates the `hvplot` panel layout object.  We specify a basemap, pick the `quadmesh` plot type for the selected variable, and indicate we want to `rasterize` the plot so that we can render massive meshes in the browser. We also specify the `groupby` parameter as the list of dimensions that remains after we remove Y and X: `ds[var].dims[:-2]`, which automatically handles variables with either dimensions [T, Y, X] or [T, Z, Y, X].  We also specify which `bokeh` controls we want to be active by default:  the `wheel_zoom` and `pan` controls.\n",
    "\n",
    "We also change the default slider to a selection widget for the `time` dimension so that specific times are easy to select.  See https://stackoverflow.com/a/54912917/2005869"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "@pn.depends(var_select, base_map_select)\n",
    "def plot(var, base_map):\n",
    "    extra_dims = list(ds[var].dims[:-2])\n",
    "    mesh = ds[var].hvplot.quadmesh(x='x', y='y', rasterize=True, crs=crs, title=var,\n",
    "                                   attr_labels=False, project=True,\n",
    "                                   groupby=extra_dims, cmap='rainbow',\n",
    "                                   width=600, height=400).opts(alpha=0.7,\n",
    "                                   data_aspect=None, active_tools=['wheel_zoom', 'pan'])\n",
    "    return pn.panel(mesh * base_map, widgets={k: pn.widgets.Select for k in extra_dims})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = pn.Column(var_select, base_map_select, plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use `.servable()` below not only to display the panel object, but to makes the panel servable outside the notebook via:  `panel serve HRRR_Dashboard.ipynb`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "col.servable('HRRR Dashboard')"
   ]
  },
  {
   "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": "3.7.3"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}