Notebook visualizing data from Hurricane Florence.

Hurricane Florence.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import metpy.calc as mpcalc\n",
    "from metpy.plots import StationPlot, add_metpy_logo, add_unidata_logo, add_timestamp\n",
    "from metpy.units import units\n",
    "from siphon.catalog import TDSCatalog\n",
    "from siphon.simplewebservice.ndbc import NDBC\n",
    "\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "import matplotlib.patheffects as mpatheffects\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from urllib.request import urlopen\n",
    "import fiona\n",
    "\n",
    "def get_zip(url):\n",
    "    data = urlopen(url).read()\n",
    "    return fiona.BytesCollection(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_track = get_zip('https://www.nhc.noaa.gov/gis/best_track/al062018_best_track.zip')\n",
    "forecast = get_zip('https://www.nhc.noaa.gov/gis/forecast/archive/al062018_5day_latest.zip')\n",
    "track_x, track_y = np.array(list(zip(*([item['geometry']['coordinates'] for _, item in best_track.items()]\n",
    "                                       + list(forecast.items())[0][1]['geometry']['coordinates']))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "buoy = NDBC.latest_observations()\n",
    "buoy.dropna(subset=['pressure', 'wind_speed', 'wind_direction'], inplace=True)\n",
    "buoy_u, buoy_v = mpcalc.wind_components(buoy['wind_speed'].values, buoy['wind_direction'].values * units.degree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat = TDSCatalog('http://thredds-test.unidata.ucar.edu/thredds/catalog/satellite/goes16/GOES16/CONUS/Channel02/current/catalog.xml')\n",
    "sat = cat.datasets[-1].remote_access(use_xarray=True)\n",
    "var = sat.metpy.parse_cf('Sectorized_CMI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sst_cat = TDSCatalog('https://www.ncei.noaa.gov/thredds/catalog/OisstBase/NetCDF/AVHRR/201809/catalog.xml')\n",
    "sst = sst_cat.datasets[-1].remote_access(use_xarray=True)\n",
    "sst_data = sst.metpy.parse_cf('sst')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rmay/repos/metpy/metpy/plots/station_plot.py:299: RuntimeWarning: invalid value encountered in subtract\n",
      "  u, v = self.ax.projection.transform_vectors(trans, self.x, self.y, u, v)\n"
     ]
    },
    {
     "data": {

The code runs in python 2.7?

True color adaptation using satpy (not in master yet): https://github.com/pytroll/pytroll-examples/blob/master/satpy/SatPy-MetPy-Siphon%20ABI%20L1B%20over%20THREDDS.ipynb