|
| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "code", |
| 5 | + "execution_count": null, |
| 6 | + "metadata": {}, |
| 7 | + "outputs": [], |
| 8 | + "source": [ |
| 9 | + "from datetime import datetime, timedelta\n", |
| 10 | + "\n", |
| 11 | + "import cartopy.crs as ccrs\n", |
| 12 | + "import cartopy.feature as cfeature\n", |
| 13 | + "import matplotlib.pyplot as plt\n", |
| 14 | + "from metpy.plots import colortables, USSTATES, USCOUNTIES\n", |
| 15 | + "import numpy as np\n", |
| 16 | + "from pyproj import Proj\n", |
| 17 | + "from siphon.catalog import TDSCatalog\n", |
| 18 | + "import xarray as xr\n", |
| 19 | + "\n", |
| 20 | + "import warnings\n", |
| 21 | + "warnings.filterwarnings(\"ignore\")" |
| 22 | + ] |
| 23 | + }, |
| 24 | + { |
| 25 | + "cell_type": "code", |
| 26 | + "execution_count": null, |
| 27 | + "metadata": {}, |
| 28 | + "outputs": [], |
| 29 | + "source": [ |
| 30 | + "def get_radar_file_url(datasets, date):\n", |
| 31 | + " '''A function to help find the desired satellite data based on the time given.\n", |
| 32 | + " \n", |
| 33 | + " Input:\n", |
| 34 | + " - List of datasets from a THREDDS Catalog\n", |
| 35 | + " - Date of desired dataset (datetime object)\n", |
| 36 | + " \n", |
| 37 | + " Output:\n", |
| 38 | + " - Index value of dataset closest to desired time\n", |
| 39 | + " '''\n", |
| 40 | + " rad_date_hour = date.strftime('%Y%m%d_%H')\n", |
| 41 | + " files = []\n", |
| 42 | + " times = []\n", |
| 43 | + " for file in cat.datasets:\n", |
| 44 | + " if rad_date_hour in file:\n", |
| 45 | + " times.append(datetime.strptime(file[-18:-5], '%Y%m%d_%H%M'))\n", |
| 46 | + " files.append(file)\n", |
| 47 | + " if not files:\n", |
| 48 | + " date = date - timedelta(hours=1)\n", |
| 49 | + " rad_date_hour = date.strftime('%Y%m%d_%H')\n", |
| 50 | + " for file in cat.datasets:\n", |
| 51 | + " if rad_date_hour in file:\n", |
| 52 | + " times.append(datetime.strptime(file[-18:-5], '%Y%m%d_%H%M'))\n", |
| 53 | + " files.append(file)\n", |
| 54 | + " find_file = np.abs(np.array(times) - date)\n", |
| 55 | + " return list(cat.datasets).index(files[np.argmin(find_file)])" |
| 56 | + ] |
| 57 | + }, |
| 58 | + { |
| 59 | + "cell_type": "code", |
| 60 | + "execution_count": null, |
| 61 | + "metadata": {}, |
| 62 | + "outputs": [], |
| 63 | + "source": [ |
| 64 | + "date = datetime.utcnow()\n", |
| 65 | + "\n", |
| 66 | + "# Create variables for URL generation\n", |
| 67 | + "station = 'KLOT'\n", |
| 68 | + "\n", |
| 69 | + "# Construct the data_url string\n", |
| 70 | + "data_url = (f'https://thredds.ucar.edu/thredds/catalog/nexrad/level2/'\n", |
| 71 | + " f'{station}/{date:%Y%m%d}/catalog.html')\n", |
| 72 | + "\n", |
| 73 | + "# Get list of files available for particular day\n", |
| 74 | + "cat = TDSCatalog(data_url)\n", |
| 75 | + "\n", |
| 76 | + "# Use homemade function to get dataset for desired time\n", |
| 77 | + "dataset = cat.datasets[get_radar_file_url(cat.datasets, date)]\n", |
| 78 | + "\n", |
| 79 | + "ds = xr.open_dataset(dataset.access_urls['OPENDAP'], decode_times=False,\n", |
| 80 | + " decode_coords=False, mask_and_scale=True)" |
| 81 | + ] |
| 82 | + }, |
| 83 | + { |
| 84 | + "cell_type": "code", |
| 85 | + "execution_count": null, |
| 86 | + "metadata": {}, |
| 87 | + "outputs": [], |
| 88 | + "source": [ |
| 89 | + "station = ds.Station\n", |
| 90 | + "slat = ds.StationLatitude\n", |
| 91 | + "slon = ds.StationLongitude\n", |
| 92 | + "elevation = ds.StationElevationInMeters\n", |
| 93 | + "vtime = datetime.strptime(ds.time_coverage_start, '%Y-%m-%dT%H:%M:%SZ')\n", |
| 94 | + "\n", |
| 95 | + "dataproj = Proj(f\"+proj=stere +lat_0={slat} +lat_ts={slat} +lon_0={slon} +ellps=WGS84 +units=m\")\n", |
| 96 | + "\n", |
| 97 | + "sweep = 0\n", |
| 98 | + "rng = ds.distanceR_HI.values\n", |
| 99 | + "az = ds.azimuthR_HI.values[sweep]\n", |
| 100 | + "ref = ds.Reflectivity_HI.values[sweep]\n", |
| 101 | + "\n", |
| 102 | + "x = rng * np.sin(np.deg2rad(az))[:, None]\n", |
| 103 | + "y = rng * np.cos(np.deg2rad(az))[:, None]\n", |
| 104 | + "\n", |
| 105 | + "lon, lat = dataproj(x, y, inverse=True)" |
| 106 | + ] |
| 107 | + }, |
| 108 | + { |
| 109 | + "cell_type": "code", |
| 110 | + "execution_count": null, |
| 111 | + "metadata": {}, |
| 112 | + "outputs": [], |
| 113 | + "source": [ |
| 114 | + "cmap = colortables.get_colortable('NWSStormClearReflectivity')\n", |
| 115 | + "\n", |
| 116 | + "fig, ax = plt.subplots(1, 1, figsize=(12, 10), subplot_kw=dict(projection=ccrs.PlateCarree()))\n", |
| 117 | + "\n", |
| 118 | + "img = ax.pcolormesh(lon, lat, ref, vmin=-30, vmax=79, cmap=cmap)\n", |
| 119 | + "plt.colorbar(img, aspect=50, pad=0)\n", |
| 120 | + "\n", |
| 121 | + "ax.set_extent([slon-2.5, slon+2.5, slat-2.5, slat+2.5], ccrs.PlateCarree())\n", |
| 122 | + "ax.set_aspect('equal', 'datalim')\n", |
| 123 | + "\n", |
| 124 | + "ax.add_feature(USCOUNTIES.with_scale('5m'), edgecolor='darkgrey')\n", |
| 125 | + "ax.add_feature(USSTATES.with_scale('5m'))\n", |
| 126 | + "\n", |
| 127 | + "plt.title(f'{station}: {ds.Reflectivity_HI.name}', loc='left')\n", |
| 128 | + "plt.title(f'Valid Time: {vtime}', loc='right')\n", |
| 129 | + "plt.show()" |
| 130 | + ] |
| 131 | + }, |
| 132 | + { |
| 133 | + "cell_type": "code", |
| 134 | + "execution_count": null, |
| 135 | + "metadata": {}, |
| 136 | + "outputs": [], |
| 137 | + "source": [] |
| 138 | + } |
| 139 | + ], |
| 140 | + "metadata": { |
| 141 | + "kernelspec": { |
| 142 | + "display_name": "Python 3", |
| 143 | + "language": "python", |
| 144 | + "name": "python3" |
| 145 | + }, |
| 146 | + "language_info": { |
| 147 | + "codemirror_mode": { |
| 148 | + "name": "ipython", |
| 149 | + "version": 3 |
| 150 | + }, |
| 151 | + "file_extension": ".py", |
| 152 | + "mimetype": "text/x-python", |
| 153 | + "name": "python", |
| 154 | + "nbconvert_exporter": "python", |
| 155 | + "pygments_lexer": "ipython3", |
| 156 | + "version": "3.8.2" |
| 157 | + } |
| 158 | + }, |
| 159 | + "nbformat": 4, |
| 160 | + "nbformat_minor": 4 |
| 161 | +} |
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