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Upper Air BUFR Soundings
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========================
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`Notebook <http://nbviewer.ipython.org/github/Unidata/python-awips/blob/master/examples/notebooks/Upper_Air_BUFR_Soundings.ipynb>`_
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The following script takes you through the steps of retrieving an Upper
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Air vertical profile from an AWIPS EDEX server and plotting a
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Skew-T/Log-P chart with Matplotlib and MetPy.
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Python-AWIPS Tutorial Notebook
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The **bufrua** plugin returns separate objects for parameters at
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**mandatory levels** and at **significant temperature levels**. For the
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Skew-T/Log-P plot, significant temperature levels are used to plot the
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pressure, temperature, and dewpoint lines, while mandatory levels are
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used to plot the wind profile.
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--------------
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Objectives
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==========
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- Retrieve an Upper Air vertical profile from EDEX
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- Plot a Skew-T/Log-P chart with
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`Matplotlib <https://matplotlib.org/>`__ and
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`MetPy <https://unidata.github.io/MetPy/latest/index.html>`__
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- Understand the **bufrua** plugin returns separate objects for
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parameters at *mandatory levels* and at *significant temperature
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levels*
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- *Significant temperature levels* are used to plot the pressure,
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temperature and dewpoint lines
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- *Mandatory levels* are used to plot the wind profile
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--------------
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Table of Contents
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-----------------
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| `1
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Imports <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#imports>`__\
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| `2 EDEX
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Connection <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#edex-connection>`__\
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| `2.1 Initial EDEX
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Connection <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#initial-edex-connection>`__\
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| `2.2 Setting Additional Request
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Parameters <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#setting-additional-request-parameters>`__\
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| `2.3 Available Location
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Names <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#available-location-names>`__\
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| `2.4 Setting the Location
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Name <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#setting-the-location-name>`__\
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| `3 Filtering by
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Time <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Sounding.html#filtering-by-time>`__\
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| `4 Get the
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Data! <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#get-the-data>`__\
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| `5 Use the
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Data! <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#use-the-data>`__\
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| `5.1 Prepare Data
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Objects <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#prepare-data-objects>`__\
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| `5.2 Convert
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Units <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#convert-units>`__\
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| `6 Plot the
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Data! <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#plot-the-data>`__\
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| `7 See
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Also <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#see-also>`__\
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| `7.1 Related
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Notebooks <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#related-notebooks>`__\
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| `7.2 Additional
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Documentation <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html#additional-documentation>`__\
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1 Imports
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---------
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The imports below are used throughout the notebook. Note the first
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import is coming directly from python-awips and allows us to connect to
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an EDEX server. The subsequent imports are for data manipulation and
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visualization.
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.. code:: ipython3
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%matplotlib inline
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from awips.dataaccess import DataAccessLayer
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import matplotlib.tri as mtri
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import matplotlib.pyplot as plt
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from mpl_toolkits.axes_grid1.inset_locator import inset_axes
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import numpy as np
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import math
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from metpy.calc import wind_speed, wind_components, lcl, dry_lapse, parcel_profile
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from metpy.calc import wind_components, lcl, parcel_profile
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from metpy.plots import SkewT, Hodograph
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from metpy.units import units, concatenate
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from metpy.units import units
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# Set host
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`Top <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html>`__
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--------------
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2 EDEX Connection
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-----------------
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2.1 Initial EDEX Connection
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~~~~~~~~~~~~~~~~~~~~~~~~~~~
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First we establish a connection to Unidata’s public EDEX server. With
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that connection made, we can create a `new data request
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object <http://unidata.github.io/python-awips/api/IDataRequest.html>`__
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and set the data type to **bufrua**, and define additional parameters
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and an identifier on the request.
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.. code:: ipython3
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# Set the edex server
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DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu")
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request = DataAccessLayer.newDataRequest()
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# Set data type
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request.setDatatype("bufrua")
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availableLocs = DataAccessLayer.getAvailableLocationNames(request)
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availableLocs.sort()
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MAN_PARAMS = set(['prMan', 'htMan', 'tpMan', 'tdMan', 'wdMan', 'wsMan'])
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2.2 Setting Additional Request Parameters
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Here we populate arrays of all the parameters that will be necessary for
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plotting the Skew-T. The ``MAN_PARAMS`` are the *mandatory levels* and
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the ``SIGT_PARAMS`` are the *significant temperature* parameters that
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were both mentioned in the `objectives section <#Objectives>`__ above.
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Also request the station name and ID to use in the figure title later
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on.
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.. code:: ipython3
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MAN_PARAMS = set(['prMan', 'tpMan', 'tdMan', 'wdMan', 'wsMan'])
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SIGT_PARAMS = set(['prSigT', 'tpSigT', 'tdSigT'])
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request.setParameters("wmoStaNum", "validTime", "rptType", "staElev", "numMand",
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"numSigT", "numSigW", "numTrop", "numMwnd", "staName")
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request.setParameters("staElev", "staName")
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request.getParameters().extend(MAN_PARAMS)
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request.getParameters().extend(SIGT_PARAMS)
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2.3 Available Location Names
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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When working with a new data type, it is often useful to investigate all
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available options for a particular setting. Shown below is how to see
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all available location names for a data request with type **bufrua**.
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This step is not necessary if you already know exactly what the location
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ID you’re interested in is.
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.. container:: alert-info
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Note: It is important to note the location names are listed by their
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WMO Station ID. Their corresponding location and site identifier can
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be looked up in this table from UNdata.
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.. code:: ipython3
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locations = DataAccessLayer.getAvailableLocationNames(request)
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locations.sort()
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print(locations)
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.. parsed-literal::
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['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', '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', '74006', '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', '94995', '94996']
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2.4 Setting the Location Name
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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In this case we’re setting the location name to the ID for ``KLBF``
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which is the North Platte Regional Airport/Lee Bird, Field in Nebraska.
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.. code:: ipython3
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# Set station ID (not name)
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request.setLocationNames("72562") #KLBF
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`Top <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html>`__
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--------------
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3 Filtering by Time
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-------------------
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Models produce many different time variants during their runs, so let’s
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limit the data to the most recent time and forecast run.
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.. code:: ipython3
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# Get all times
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datatimes = DataAccessLayer.getAvailableTimes(request)
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`Top <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html>`__
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--------------
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4 Get the Data!
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---------------
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Here we can now request our data response from the EDEX server with our
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defined time filter.
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Printing out some data from the first object in the response array can
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help verify we received the data we were interested in.
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.. code:: ipython3
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# Get most recent record
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response = DataAccessLayer.getGeometryData(request,times=datatimes[-1].validPeriod)
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obj = response[0]
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print("parms = " + str(obj.getParameters()))
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print("site = " + str(obj.getLocationName()))
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print("geom = " + str(obj.getGeometry()))
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print("datetime = " + str(obj.getDataTime()))
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print("reftime = " + str(obj.getDataTime().getRefTime()))
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print("fcstHour = " + str(obj.getDataTime().getFcstTime()))
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print("period = " + str(obj.getDataTime().getValidPeriod()))
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.. parsed-literal::
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parms = ['staElev', 'staName']
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site = 72562
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geom = POINT (-100.7005615234375 41.14971923828125)
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datetime = 2023-05-19 12:00:00
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reftime = May 19 23 12:00:00 GMT
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fcstHour = 0
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period = (May 19 23 12:00:00 , May 19 23 12:00:00 )
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`Top <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html>`__
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--------------
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5 Use the Data!
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---------------
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Since we filtered on time, and requested the data in the previous cell,
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we now have a ``response`` object we can work with.
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5.1 Prepare data objects
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~~~~~~~~~~~~~~~~~~~~~~~~
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Here we construct arrays for each parameter to plot (temperature,
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pressure, and wind components). After populating each of the arrays, we
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sort and mask missing data.
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.. code:: ipython3
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# Initialize data arrays
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tdMan,tpMan,prMan,wdMan,wsMan = np.array([]),np.array([]),np.array([]),np.array([]),np.array([])
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@ -94,6 +272,16 @@ used to plot the wind profile.
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direc[direc <= -9999] = np.nan
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spd[spd <= -9999] = np.nan
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5.2 Convert Units
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~~~~~~~~~~~~~~~~~
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We need to modify the units several of the data parameters are returned
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in. Here we convert Temperature from Fahrenheit to Celcius, convert
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pressure to milibars, and extract wind for both the u and v directional
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components in Knots and Radians.
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.. code:: ipython3
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# assign units
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p = (prSig/100) * units.mbar
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wpres = (wpres/100) * units.mbar
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tman = tman * units.degC
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dman = dman * units.degC
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`Top <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html>`__
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--------------
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6 Plot the Data!
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----------------
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Create and display SkewT and Hodograph plots using MetPy.
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.. code:: ipython3
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# Create SkewT/LogP
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plt.rcParams['figure.figsize'] = (10, 12)
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skew = SkewT()
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@ -144,5 +343,48 @@ used to plot the wind profile.
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.. image:: Upper_Air_BUFR_Soundings_files/Upper_Air_BUFR_Soundings_1_0.png
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.. image:: Upper_Air_BUFR_Soundings_files/Upper_Air_BUFR_Soundings_28_0.png
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`Top <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html>`__
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--------------
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7 See Also
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----------
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7.1 Related Notebooks
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~~~~~~~~~~~~~~~~~~~~~
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- `Grid Levels and
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Parameters <https://unidata.github.io/python-awips/examples/generated/Grid_Levels_and_Parameters.html>`__
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- `Model Sounding
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Data <http://unidata.github.io/python-awips/examples/generated/Model_Sounding_Data.html>`__
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- `Forecast Model Vertical
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Sounding <http://unidata.github.io/python-awips/examples/generated/Forecast_Model_Vertical_Sounding.html>`__
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7.2 Additional Documentation
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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**python-awips:**
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- `awips.DataAccessLayer <http://unidata.github.io/python-awips/api/DataAccessLayer.html>`__
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- `awips.PyGeometryData <http://unidata.github.io/python-awips/api/PyGeometryData.html>`__
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**matplotlib:**
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- `matplotlib.pyplot <https://matplotlib.org/3.3.3/api/_as_gen/matplotlib.pyplot.html>`__
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**MetPy**
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- `metpy.wind_components <https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.wind_components.html>`__
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- `metpy.lcl <https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.lcl.html>`__
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(Lifted Condensation Level)
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- `metpy.parcel_profile <https://unidata.github.io/MetPy/latest/api/generated/metpy.calc.parcel_profile.html>`__
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- `metpy.skewt <https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html>`__
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- `metpy.hodograph <https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.Hodograph.html>`__
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`Top <https://unidata.github.io/python-awips/examples/generated/Upper_Air_BUFR_Soundings.html>`__
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--------------
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