{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Python-AWIPS Tutorial Notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"\n",
"# Objectives\n",
"\n",
"* Retrieve an Upper Air vertical profile from EDEX\n",
"* Plot a Skew-T/Log-P chart with [Matplotlib](https://matplotlib.org/) and [MetPy](https://unidata.github.io/MetPy/latest/index.html)\n",
"* Understand the **bufrua** plugin returns separate objects for parameters at *mandatory levels* and at *significant temperature levels*\n",
" * *Significant temperature levels* are used to plot the pressure, temperature and dewpoint lines\n",
" * *Mandatory levels* are used to plot the wind profile\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Table of Contents\n",
"\n",
"[1 Imports](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#imports)
\n",
"[2 EDEX Connection](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#edex-connection)
\n",
" [2.1 Initial EDEX Connection](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#initial-edex-connection)
\n",
" [2.2 Setting Additional Request Parameters](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#setting-additional-request-parameters)
\n",
" [2.3 Available Location Names](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#available-location-names)
\n",
" [2.4 Setting the Location Name](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#setting-the-location-name)
\n",
"[3 Filtering by Time](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#filtering-by-time)
\n",
"[4 Get the Data!](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#get-the-data)
\n",
"[5 Use the Data!](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#use-the-data)
\n",
" [5.1 Prepare Data Objects](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#prepare-data-objects)
\n",
" [5.2 Convert Units](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#convert-units)
\n",
"[6 Plot the Data!](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#plot-the-data)
\n",
"[7 See Also](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#see-also)
\n",
" [7.1 Related Notebooks](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#related-notebooks)
\n",
" [7.2 Additional Documentation](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#additional-documentation)
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1 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,
"metadata": {},
"outputs": [],
"source": [
"from awips.dataaccess import DataAccessLayer\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
"import numpy as np\n",
"from metpy.calc import wind_components, lcl, parcel_profile\n",
"from metpy.plots import SkewT, Hodograph\n",
"from metpy.units import units"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Top](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html)\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2 EDEX Connection\n",
"\n",
"### 2.1 Initial EDEX Connection\n",
"\n",
"First we establish a connection to Unidata's public EDEX server. With that connection made, we can create a [new data request object](http://unidata.github.io/python-awips/api/IDataRequest.html) and set the data type to ***bufrua***, and define additional parameters and an identifier on the request."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Set the edex server\n",
"DataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")\n",
"request = DataAccessLayer.newDataRequest()\n",
"\n",
"# Set data type\n",
"request.setDatatype(\"bufrua\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2 Setting Additional Request Parameters\n",
"\n",
"Here we populate arrays of all the parameters that will be necessary for plotting the Skew-T. The `MAN_PARAMS` are the *mandatory levels* and the `SIGT_PARAMS` are the *significant temperature* parameters that were both mentioned in the [objectives section](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#objectives) above. \n",
"\n",
"Also request the station name and elevation to use in the figure title later on."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"MAN_PARAMS = set(['prMan', 'wdMan', 'wsMan'])\n",
"SIGT_PARAMS = set(['prSigT', 'tpSigT', 'tdSigT'])\n",
"request.setParameters(\"staElev\", \"staName\")\n",
"request.getParameters().extend(MAN_PARAMS)\n",
"request.getParameters().extend(SIGT_PARAMS)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.3 Available Location Names\n",
"When working with a new data type, it is often useful to investigate all available options for a particular setting. Shown below is how to see all available location names for a data request with type **bufrua**. This step is not necessary if you already know exactly what the location ID you're interested in is."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">**Note:** It is important to note the location names are listed by their WMO Station ID. Their corresponding location and site identifier can be looked up in [this table from UNdata](https://data.un.org/Data.aspx?d=CLINO&f=ElementCode%3a15%3bCountryCode%3aUS&c=2,5,6,7,10,15,18,19,20,22,24,26,28,30,32,34,36,38,40,42,44,46&s=CountryName:asc,WmoStationNumber:asc,StatisticCode:asc&v=1)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['21824', '21946', '24266', '24343', '24641', '24688', '24959', '25123', '25703', '25913', '31004', '31088', '31300', '31369', '31510', '31538', '31770', '31873', '32061', '32098', '32150', '32389', '32477', '32540', '32618', '47122', '47138', '47158', '47401', '47412', '47582', '47646', '47678', '47807', '47827', '47909', '47918', '47945', '47971', '47991', '70026', '70133', '70200', '70219', '70231', '70261', '70273', '70308', '70316', '70326', '70350', '70361', '70398', '70414', '71043', '71081', '71082', '71109', '71119', '71603', '71722', '71802', '71811', '71815', '71816', '71823', '71845', '71867', '71906', '71907', '71909', '71913', '71917', '71924', '71925', '71926', '71934', '71945', '71957', '71964', '72201', '72202', '72206', '72208', '72210', '72214', '72215', '72221', '72230', '72233', '72235', '72240', '72248', '72249', '72250', '72251', '72261', '72265', '72274', '72293', '72305', '72317', '72318', '72327', '72340', '72357', '72363', '72364', '72365', '72376', '72381', '72388', '72393', '72402', '72403', '72426', '72440', '72451', '72456', '72469', '72476', '72489', '72493', '72501', '72518', '72520', '72528', '72558', '72562', '72572', '72582', '72597', '72632', '72634', '72645', '72649', '72659', '72662', '72672', '72681', '72694', '72712', '72747', '72764', '72768', '72776', '72786', '72797', '74004', '74005', '74389', '74455', '74560', '74794', '78016', '78384', '78397', '78486', '78526', '78583', '78866', '78954', '78970', '78988', '80001', '91165', '91212', '91285', '91334', '91348', '91366', '91376', '91408', '91413', '91610', '91643', '91680', '91765', '94120', '94203', '94299', '94332', '94461', '94510', '94578', '94637', '94638', '94653', '94659', '94672', '94711', '94776', '94996']\n"
]
}
],
"source": [
"locations = DataAccessLayer.getAvailableLocationNames(request)\n",
"locations.sort()\n",
"print(locations)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4 Setting the Location Name\n",
"\n",
"In this case we're setting the location name to the ID for `KLBF` which is the North Platte Regional Airport/Lee Bird, Field in Nebraska."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Set station ID (not name)\n",
"request.setLocationNames(\"72562\") #KLBF"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Top](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html)\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3 Filtering by Time\n",
"\n",
"Models produce many different time variants during their runs, so let's limit the data to the most recent time and forecast run."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Get all times\n",
"datatimes = DataAccessLayer.getAvailableTimes(request)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Top](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html)\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4 Get the Data!\n",
"\n",
"Here we can now request our data response from the EDEX server with our defined time filter.\n",
"\n",
"Printing out some data from the first object in the response array can help verify we received the data we were interested in."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"parms = ['tpSigT', 'prSigT', 'tdSigT']\n",
"site = 72562\n",
"geom = POINT (-100.7005615234375 41.14971923828125)\n",
"datetime = 2023-05-25 12:00:00\n",
"reftime = May 25 23 12:00:00 GMT\n",
"fcstHour = 0\n",
"period = (May 25 23 12:00:00 , May 25 23 12:00:00 )\n"
]
}
],
"source": [
"# Get most recent record\n",
"response = DataAccessLayer.getGeometryData(request,times=datatimes[-1].validPeriod)\n",
"obj = response[0]\n",
"\n",
"print(\"parms = \" + str(obj.getParameters()))\n",
"print(\"site = \" + str(obj.getLocationName()))\n",
"print(\"geom = \" + str(obj.getGeometry()))\n",
"print(\"datetime = \" + str(obj.getDataTime()))\n",
"print(\"reftime = \" + str(obj.getDataTime().getRefTime()))\n",
"print(\"fcstHour = \" + str(obj.getDataTime().getFcstTime()))\n",
"print(\"period = \" + str(obj.getDataTime().getValidPeriod()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Top](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html)\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5 Use the Data!\n",
"\n",
"Since we filtered on time, and requested the data in the previous cell, we now have a `response` object we can work with.\n",
"\n",
"### 5.1 Prepare Data Objects\n",
"\n",
"Here we construct arrays for each parameter to plot (temperature, dewpoint, pressure, and wind components).\n",
"After populating each of the arrays, we sort and mask missing data."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Initialize data arrays\n",
"prMan,wdMan,wsMan = np.array([]),np.array([]),np.array([])\n",
"prSig,tpSig,tdSig = np.array([]),np.array([]),np.array([])\n",
"manGeos = []\n",
"sigtGeos = []\n",
"\n",
"# Build arrays\n",
"for ob in response:\n",
" parm_array = ob.getParameters()\n",
" if set(parm_array) & MAN_PARAMS:\n",
" manGeos.append(ob)\n",
" prMan = np.append(prMan,ob.getNumber(\"prMan\"))\n",
" wdMan = np.append(wdMan,ob.getNumber(\"wdMan\"))\n",
" wsMan, wsUnit = np.append(wsMan,ob.getNumber(\"wsMan\")), ob.getUnit(\"wsMan\")\n",
" continue\n",
" if set(parm_array) & SIGT_PARAMS:\n",
" sigtGeos.append(ob)\n",
" prSig = np.append(prSig,ob.getNumber(\"prSigT\"))\n",
" tpSig = np.append(tpSig,ob.getNumber(\"tpSigT\"))\n",
" tpUnit = ob.getUnit(\"tpSigT\")\n",
" tdSig = np.append(tdSig,ob.getNumber(\"tdSigT\"))\n",
" continue\n",
"\n",
"# Sort mandatory levels (but not sigT levels) because of the 1000.MB interpolation inclusion\n",
"ps = prMan.argsort()[::-1]\n",
"wpres = prMan[ps]\n",
"direc = wdMan[ps]\n",
"spd = wsMan[ps]\n",
"\n",
"# Flag missing data\n",
"prSig[prSig <= -9999] = np.nan\n",
"tpSig[tpSig <= -9999] = np.nan\n",
"tdSig[tdSig <= -9999] = np.nan\n",
"wpres[wpres <= -9999] = np.nan\n",
"direc[direc <= -9999] = np.nan\n",
"spd[spd <= -9999] = np.nan"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.2 Convert Units\n",
"\n",
"We need to modify the units several of the data parameters are returned in. Here we convert the units for Temperature and Dewpoint from Kelvin to Celsius, convert pressure to milibars, and extract wind for both the u and v directional components in Knots and Radians. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# assign units\n",
"p = (prSig/100) * units.mbar\n",
"wpres = (wpres/100) * units.mbar\n",
"u,v = wind_components(spd * units.knots, np.deg2rad(direc))\n",
"\n",
"if tpUnit == 'K':\n",
" T = (tpSig-273.15) * units.degC\n",
" Td = (tdSig-273.15) * units.degC"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Top](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html)\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6 Plot the Data!\n",
"\n",
"Create and display SkewT and Hodograph plots using MetPy."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Create SkewT/LogP\n",
"plt.rcParams['figure.figsize'] = (10, 12)\n",
"skew = SkewT()\n",
"skew.plot(p, T, 'r', linewidth=2)\n",
"skew.plot(p, Td, 'g', linewidth=2)\n",
"skew.plot_barbs(wpres, u, v)\n",
"skew.ax.set_ylim(1000, 100)\n",
"skew.ax.set_xlim(-60, 30)\n",
"\n",
"title_string = \" T(F) Td \" \n",
"title_string += \" \" + str(ob.getString(\"staName\"))\n",
"title_string += \" \" + str(ob.getDataTime().getRefTime())\n",
"title_string += \" (\" + str(ob.getNumber(\"staElev\")) + \"m elev)\"\n",
"title_string += \"\\n\" + str(round(T[0].to('degF').item(),1))\n",
"title_string += \" \" + str(round(Td[0].to('degF').item(),1))\n",
"plt.title(title_string, loc='left')\n",
"\n",
"# Calculate LCL height and plot as black dot\n",
"lcl_pressure, lcl_temperature = lcl(p[0], T[0], Td[0])\n",
"skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')\n",
"\n",
"# Calculate full parcel profile and add to plot as black line\n",
"prof = parcel_profile(p, T[0], Td[0]).to('degC')\n",
"skew.plot(p, prof, 'k', linewidth=2)\n",
"\n",
"# An example of a slanted line at constant T -- in this case the 0 isotherm\n",
"l = skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)\n",
"\n",
"# Draw hodograph\n",
"ax_hod = inset_axes(skew.ax, '30%', '30%', loc=3)\n",
"h = Hodograph(ax_hod, component_range=max(wsMan))\n",
"h.add_grid(increment=20)\n",
"h.plot_colormapped(u, v, spd)\n",
"\n",
"# Show the plot\n",
"plt.show()"
]
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"[Top](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html)\n",
"\n",
"---"
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"## 7 See Also\n",
"\n",
"### 7.1 Related Notebooks\n",
"\n",
"* [Grid Levels and Parameters](https://unidata.github.io/python-awips/examples/generated/Grid_Levels_and_Parameters.html)\n",
"* [Model Sounding Data](http://unidata.github.io/python-awips/examples/generated/Model_Sounding_Data.html)\n",
"* [Forecast Model Vertical Sounding](http://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html)\n",
"\n",
"### 7.2 Additional Documentation\n",
"\n",
"**python-awips:**\n",
"\n",
"* [awips.DataAccessLayer](http://unidata.github.io/python-awips/api/DataAccessLayer.html)\n",
"* [awips.PyGeometryData](http://unidata.github.io/python-awips/api/PyGeometryData.html)\n",
"\n",
"**matplotlib:**\n",
"\n",
"* [matplotlib.pyplot](https://matplotlib.org/3.3.3/api/_as_gen/matplotlib.pyplot.html)\n",
"\n",
"**MetPy**\n",
"\n",
"* [metpy.wind_components](https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.wind_components.html)\n",
"* [metpy.lcl](https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.lcl.html) (Lifted Condensation Level)\n",
"* [metpy.parcel_profile](https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.parcel_profile.html)\n",
"* [metpy.skewt](https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html)\n",
"* [metpy.hodograph](https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.Hodograph.html)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Top](https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html)\n",
"\n",
"---"
]
}
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