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{
"cells": [
{
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"source": [
"<a name=\"top\"></a>\n",
"<div style=\"width:1000 px\">\n",
"\n",
"<div style=\"float:right; width:98 px; height:98px;\">\n",
"<img src=\"https://docs.unidata.ucar.edu/images/logos/unidata_logo_vertical_150x150.png\" alt=\"Unidata Logo\" style=\"height: 98px;\">\n",
"</div>\n",
"\n",
"# Precipitation Accumulation Region of Interest\n",
"**Python-AWIPS Tutorial Notebook**\n",
"\n",
"<div style=\"clear:both\"></div>\n",
"</div>\n",
"\n",
"---\n",
"\n",
"<div style=\"float:right; width:250 px\"><img src=\"../images/precip_roi_preview.png\" alt=\"Colorized Precipation Accumulation with Identified Region of Interest\" style=\"height: 300px;\"></div>\n",
"\n",
"\n",
"# Objectives\n",
"\n",
"* Access the model data from an EDEX server and limit the data returned by using model specific parameters\n",
"* Calculate the total precipitation over several model runs\n",
"* Create a colorized plot for the continental US of the accumulated precipitation data\n",
"* Calculate and identify area of highest of precipitation\n",
"* Use higher resolution data to draw region of interest\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"toc": true
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"source": [
"<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
"<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Imports\" data-toc-modified-id=\"Imports-1\"><span class=\"toc-item-num\">1 </span>Imports</a></span></li><li><span><a href=\"#Initial-Setup\" data-toc-modified-id=\"Initial-Setup-2\"><span class=\"toc-item-num\">2 </span>Initial Setup</a></span><ul class=\"toc-item\"><li><span><a href=\"#Geographic-Filter\" data-toc-modified-id=\"Geographic-Filter-2.1\"><span class=\"toc-item-num\">2.1 </span>Geographic Filter</a></span></li><li><span><a href=\"#EDEX-Connection\" data-toc-modified-id=\"EDEX-Connection-2.2\"><span class=\"toc-item-num\">2.2 </span>EDEX Connection</a></span></li><li><span><a href=\"#Refine-the-Request\" data-toc-modified-id=\"Refine-the-Request-2.3\"><span class=\"toc-item-num\">2.3 </span>Refine the Request</a></span></li><li><span><a href=\"#Get-Times\" data-toc-modified-id=\"Get-Times-2.4\"><span class=\"toc-item-num\">2.4 </span>Get Times</a></span></li></ul></li><li><span><a href=\"#Function:-calculate_accumulated_precip()\" data-toc-modified-id=\"Function:-calculate_accumulated_precip()-3\"><span class=\"toc-item-num\">3 </span>Function: calculate_accumulated_precip()</a></span></li><li><span><a href=\"#Fuction:-make_map()\" data-toc-modified-id=\"Fuction:-make_map()-4\"><span class=\"toc-item-num\">4 </span>Fuction: make_map()</a></span></li><li><span><a href=\"#Get-the-Data!\" data-toc-modified-id=\"Get-the-Data!-5\"><span class=\"toc-item-num\">5 </span>Get the Data!</a></span></li><li><span><a href=\"#Plot-the-Data!\" data-toc-modified-id=\"Plot-the-Data!-6\"><span class=\"toc-item-num\">6 </span>Plot the Data!</a></span><ul class=\"toc-item\"><li><span><a href=\"#Create-CONUS-Image\" data-toc-modified-id=\"Create-CONUS-Image-6.1\"><span class=\"toc-item-num\">6.1 </span>Create CONUS Image</a></span></li><li><span><a href=\"#Create-Region-of-Interest-Image\" data-toc-modified-id=\"Create-Region-of-Interest-Image-6.2\"><span class=\"toc-item-num\">6.2 </span>Create Region of Interest Image</a></span></li></ul></li><li><span><a href=\"#High-Resolution-ROI\" data-toc-modified-id=\"High-Resolution-ROI-7\"><span class=\"toc-item-num\">7 </span>High Resolution ROI</a></span><ul class=\"toc-item\"><li><span><a href=\"#New-Data-Request\" data-toc-modified-id=\"New-Data-Request-7.1\"><span class=\"toc-item-num\">7.1 </span>New Data Request</a></span></li><li><span><a href=\"#Calculate-Data\" data-toc-modified-id=\"Calculate-Data-7.2\"><span class=\"toc-item-num\">7.2 </span>Calculate Data</a></span></li><li><span><a href=\"#Plot-ROI\" data-toc-modified-id=\"Plot-ROI-7.3\"><span class=\"toc-item-num\">7.3 </span>Plot ROI</a></span></li></ul></li><li><span><a href=\"#See-Also\" data-toc-modified-id=\"See-Also-8\"><span class=\"toc-item-num\">8 </span>See Also</a></span><ul class=\"toc-item\"><li><span><a href=\"#Related-Notebooks\" data-toc-modified-id=\"Related-Notebooks-8.1\"><span class=\"toc-item-num\">8.1 </span>Related Notebooks</a></span></li><li><span><a href=\"#Additional-Documentation\" data-toc-modified-id=\"Additional-Documentation-8.2\"><span class=\"toc-item-num\">8.2 </span>Additional Documentation</a></span></li></ul></li></ul></div>"
]
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"## Imports\n",
"\n",
"The imports below are used throughout the notebook. Note the first import is coming directly from python-awips and allows us to connect to an EDEX server. The subsequent imports are for data manipulation and visualization."
]
},
{
"cell_type": "code",
"execution_count": 1,
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"source": [
"from awips.dataaccess import DataAccessLayer\n",
"import cartopy.crs as ccrs\n",
"import matplotlib.pyplot as plt\n",
"from metpy.units import units\n",
"import numpy as np\n",
"from shapely.geometry import Point, Polygon"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"#top\">Top</a>\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initial Setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Geographic Filter\n",
"\n",
"By defining a bounding box for the Continental US (CONUS), we’
]
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{
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"execution_count": 2,
"metadata": {},
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"source": [
"conus=[-125, -65, 25, 55]\n",
"conus_envelope = Polygon([(conus[0],conus[2]),(conus[0],conus[3]),\n",
" (conus[1],conus[3]),(conus[1],conus[2]),\n",
" (conus[0],conus[2])])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### EDEX Connection\n",
"\n",
"First we establish a connection to Unidata’
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
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"DataAccessLayer.changeEDEXHost(\"edex-beta.unidata.ucar.edu\")\n",
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"request = DataAccessLayer.newDataRequest(\"grid\", envelope=conus_envelope)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Refine the Request\n",
"\n",
"Here we specify which model we're interested in by setting the *LocationNames*, and the specific data we're interested in by setting the *Levels* and *Parameters*."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"request.setLocationNames(\"GFS1p0\")\n",
"request.setLevels(\"0.0SFC\")\n",
"request.setParameters(\"TP\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get Times\n",
"\n",
"We need to get the available times and cycles for our model data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
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"outputs": [
{
"ename": "IndexError",
"evalue": "list index out of range",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[5], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m cycles \u001b[38;5;241m=\u001b[39m DataAccessLayer\u001b[38;5;241m.\u001b[39mgetAvailableTimes(request, \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 2\u001b[0m times \u001b[38;5;241m=\u001b[39m DataAccessLayer\u001b[38;5;241m.\u001b[39mgetAvailableTimes(request)\n\u001b[0;32m----> 3\u001b[0m fcstRun \u001b[38;5;241m=\u001b[39m DataAccessLayer\u001b[38;5;241m.\u001b[39mgetForecastRun(\u001b[43mcycles\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m, times)\n",
"\u001b[0;31mIndexError\u001b[0m: list index out of range"
]
}
],
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"source": [
"cycles = DataAccessLayer.getAvailableTimes(request, True)\n",
"times = DataAccessLayer.getAvailableTimes(request)\n",
"fcstRun = DataAccessLayer.getForecastRun(cycles[-1], times)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"#top\">Top</a>\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Function: calculate_accumulated_precip()\n",
"\n",
"Since we'll want to calculate the accumulated precipitation of our data more than once, it makes sense to create a function that we can call instead of duplicating the logic.\n",
"\n",
"This function cycles through all the grid data responses and adds up all of the rainfall to produce a numpy array with the total ammount of rainfall for the given data request. It also finds the maximum rainfall point in x and y coordinates."
]
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"def calculate_accumulated_precip(dataRequest, forecastRun):\n",
"\n",
" for i, tt in enumerate(forecastRun):\n",
" response = DataAccessLayer.getGridData(dataRequest, [tt])\n",
" grid = response[0]\n",
" if i>0:\n",
" data += grid.getRawData()\n",
" else:\n",
" data = grid.getRawData()\n",
" data[data <= -9999] = 0\n",
" print(data.min(), data.max(), grid.getDataTime().getFcstTime()/3600)\n",
"\n",
" # Convert from mm to inches\n",
" result = (data * units.mm).to(units.inch)\n",
" \n",
" ii,jj = np.where(result==result.max())\n",
" i=ii[0]\n",
" j=jj[0]\n",
"\n",
" return result, i, j"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"#top\">Top</a>\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fuction: make_map()\n",
"\n",
"This function creates the basics of the map we're going to plot our data on. It takes in a bounding box to determine the extent and then adds coastlines for easy frame of reference."
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"source": [
"def make_map(bbox, projection=ccrs.PlateCarree()):\n",
" fig, ax = plt.subplots(figsize=(20, 14),\n",
" subplot_kw=dict(projection=projection))\n",
" ax.set_extent(bbox)\n",
" ax.coastlines(resolution='50m')\n",
" return fig, ax"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"#top\">Top</a>\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get the Data!\n",
"\n",
"Access the data from the DataAccessLayer interface using the *getGridData* function. Use that data to calculate the accumulated rainfall, the maximum rainfall point, and the region of interest bounding box."
]
},
{
"cell_type": "code",
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"execution_count": null,
"metadata": {},
"outputs": [],
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"source": [
"## get the grid response from edex\n",
"response = DataAccessLayer.getGridData(request, [fcstRun[-1]]) \n",
"## take the first result to get the location information from\n",
"grid = response[0]\n",
"\n",
"## get the location coordinates and create a bounding box for our map\n",
"lons, lats = grid.getLatLonCoords()\n",
"bbox = [lons.min(), lons.max(), lats.min(), lats.max()]\n",
"fcstHr = int(grid.getDataTime().getFcstTime()/3600)\n",
"\n",
"## calculate the total precipitation\n",
"tp_inch, i, j = calculate_accumulated_precip(request, fcstRun)\n",
"print(tp_inch.min(), tp_inch.max())\n",
"\n",
"## use the max points coordinates to get the max point in lat/lon coords\n",
"maxPoint = Point(lons[i][j], lats[i][j])\n",
"inc = 3.5\n",
"## create a region of interest bounding box\n",
"roi_box=[maxPoint.x-inc, maxPoint.x+inc, maxPoint.y-inc, maxPoint.y+inc]\n",
"roi_polygon = Polygon([(roi_box[0],roi_box[2]),(roi_box[0],roi_box[3]), \n",
" (roi_box[1],roi_box[3]),(roi_box[1],roi_box[2]),(roi_box[0],roi_box[2])])\n",
"\n",
"print(maxPoint)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"#top\">Top</a>\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot the Data!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create CONUS Image\n",
"\n",
"Plot our data on our CONUS map."
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"cmap = plt.get_cmap('rainbow')\n",
"fig, ax = make_map(bbox=bbox)\n",
"cs = ax.pcolormesh(lons, lats, tp_inch, cmap=cmap)\n",
"cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal')\n",
"cbar.set_label(grid.getLocationName() + \" Total accumulated precipitation in inches, \" \\\n",
" + str(fcstHr) + \"-hr fcst valid \" + str(grid.getDataTime().getRefTime()))\n",
"\n",
"ax.scatter(maxPoint.x, maxPoint.y, s=300,\n",
" transform=ccrs.PlateCarree(),marker=\"+\",facecolor='black')\n",
"\n",
"ax.add_geometries([roi_polygon], ccrs.PlateCarree(), facecolor='none', edgecolor='white', linewidth=2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Region of Interest Image\n",
"\n",
"Now crop the data and zoom in on the region of interest (ROI) to create a new plot."
]
},
{
"cell_type": "code",
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"execution_count": null,
"metadata": {},
"outputs": [],
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"source": [
"# cmap = plt.get_cmap('rainbow')\n",
"fig, ax = make_map(bbox=roi_box)\n",
"\n",
"cs = ax.pcolormesh(lons, lats, tp_inch, cmap=cmap)\n",
"cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal')\n",
"cbar.set_label(grid.getLocationName() + \" Total accumulated precipitation in inches, \" \\\n",
" + str(fcstHr) + \"-hr fcst valid \" + str(grid.getDataTime().getRefTime()))\n",
"\n",
"ax.scatter(maxPoint.x, maxPoint.y, s=300,\n",
" transform=ccrs.PlateCarree(),marker=\"+\",facecolor='black')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"#top\">Top</a>\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## High Resolution ROI"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### New Data Request\n",
"\n",
"To see the region of interest more clearly, we can redo the process with a higher resolution model (GFS20 vs. GFS1.0)."
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
"outputs": [],
"source": [
"roiRequest = DataAccessLayer.newDataRequest(\"grid\", envelope=conus_envelope)\n",
"roiRequest.setLocationNames(\"GFS20\")\n",
"roiRequest.setLevels(\"0.0SFC\")\n",
"roiRequest.setParameters(\"TP\")\n",
"\n",
"roiCycles = DataAccessLayer.getAvailableTimes(roiRequest, True)\n",
"roiTimes = DataAccessLayer.getAvailableTimes(roiRequest)\n",
"roiFcstRun = DataAccessLayer.getForecastRun(roiCycles[-1], roiTimes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculate Data"
]
},
{
"cell_type": "code",
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"execution_count": null,
"metadata": {},
"outputs": [],
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"source": [
"roiResponse = DataAccessLayer.getGridData(roiRequest, [roiFcstRun[-1]]) \n",
"print(roiResponse)\n",
"roiGrid = roiResponse[0]\n",
"\n",
"roiLons, roiLats = roiGrid.getLatLonCoords()\n",
"\n",
"roi_data, i, j = calculate_accumulated_precip(roiRequest, roiFcstRun)\n",
"\n",
"roiFcstHr = int(roiGrid.getDataTime().getFcstTime()/3600)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot ROI"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"# cmap = plt.get_cmap('rainbow')\n",
"fig, ax = make_map(bbox=roi_box)\n",
"\n",
"cs = ax.pcolormesh(roiLons, roiLats, roi_data, cmap=cmap)\n",
"cbar = fig.colorbar(cs, shrink=0.7, orientation='horizontal')\n",
"cbar.set_label(roiGrid.getLocationName() + \" Total accumulated precipitation in inches, \" \\\n",
" + str(roiFcstHr) + \"-hr fcst valid \" + str(roiGrid.getDataTime().getRefTime()))\n",
"\n",
"ax.scatter(maxPoint.x, maxPoint.y, s=300,\n",
" transform=ccrs.PlateCarree(),marker=\"+\",facecolor='black')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"#top\">Top</a>\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## See Also\n",
"\n",
"### Related Notebooks\n",
"\n",
"* [Colorized Grid Data](https://unidata.github.io/python-awips/examples/generated/Colorized_Grid_Data.html)\n",
"* [Grid Levels and Parameters](https://unidata.github.io/python-awips/examples/generated/Grid_Levels_and_Parameters.html)\n",
"\n",
"### Additional Documentation\n",
"\n",
"**python-awips:**\n",
"* [awips.DataAccessLayer](http://unidata.github.io/python-awips/api/DataAccessLayer.html)\n",
"* [awips.PyGridData](http://unidata.github.io/python-awips/api/PyGridData.html)\n",
"\n",
"**matplotlib:**\n",
"* [matplotlib.pyplot](https://matplotlib.org/3.3.3/api/_as_gen/matplotlib.pyplot.html)\n",
"* [matplotlib.pyplot.subplot](https://matplotlib.org/3.3.3/api/_as_gen/matplotlib.pyplot.subplot.html)\n",
"* [matplotlib.pyplot.pcolormesh](https://matplotlib.org/3.3.3/api/_as_gen/matplotlib.pyplot.pcolormesh.html)"
]
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"source": [
"<a href=\"#top\">Top</a>\n",
"\n",
"---"
]
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