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METAR main

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Notebook -.. container:

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.. container::
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-.. rubric:: METAR Staion Plot with MetPy
-   :name: metar-staion-plot-with-metpy
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-**Python-AWIPS Tutorial Notebook**
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-.. container::
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Objectives

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  • Use python-awips to connect to an edex server

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  • Define and filter data request for METAR surface obs

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  • Extract necessary data and reformat it for plotting

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  • Stylize and plot METAR station data using Cartopy, Matplotlib, and -MetPy

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Table of Contents

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  • 1  Imports

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  • 2  Function: get_cloud_cover()

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  • 3  Initial Setup

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    • 3.1  Initial EDEX Connection

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    • 3.2  Setting Connection Location Names

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  • 4  Filter by Time

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  • 5  Use the Data!

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    • 5.1  Get the Data!

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    • 5.2  Extract all Parameters

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    • 5.3  Populate the Data Dictionary

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  • 6  Plot the Data!

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  • 7  See Also

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    • 7.1  Related Notebooks

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    • 7.2  Additional Documentation

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Imports

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The imports below are used throughout the notebook. Note the first two -imports are coming directly from python-awips and allow us to connect to -an EDEX server, and define a timrange used for filtering the data. The -subsequent imports are for data manipulation and visualization.

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from awips.dataaccess import DataAccessLayer
-from dynamicserialize.dstypes.com.raytheon.uf.common.time import TimeRange
-from datetime import datetime, timedelta
-import numpy as np
-import cartopy.crs as ccrs
-import cartopy.feature as cfeature
-import matplotlib.pyplot as plt
-from metpy.calc import wind_components
-from metpy.plots import StationPlot, StationPlotLayout, sky_cover
-from metpy.units import units
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Function: get_cloud_cover()

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Returns the cloud fraction values as integer codes (0 through 8).

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def get_cloud_cover(code):
-    if 'OVC' in code:
-        return 8
-    elif 'BKN' in code:
-        return 6
-    elif 'SCT' in code:
-        return 4
-    elif 'FEW' in code:
-        return 2
-    else:
-        return 0
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Initial Setup

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Initial EDEX Connection

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First we establish a connection to Unidata’s public EDEX server. With -that connection made, we can create a new data request -object -and set the data type to obs.

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Then, because we’re going to uses MetPy’s -StationPlot -and -StationPlotLayout -we need to define several parameters, and then set them on the data -request object.

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# EDEX Request
-edexServer = "edex-cloud.unidata.ucar.edu"
-DataAccessLayer.changeEDEXHost(edexServer)
-request = DataAccessLayer.newDataRequest("obs")
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-# define desired parameters
-single_value_params = ["timeObs", "stationName", "longitude", "latitude",
-                       "temperature", "dewpoint", "windDir",
-                       "windSpeed"]
-multi_value_params = ["skyCover"]
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-params = single_value_params + multi_value_params
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-# set all parameters on the request
-request.setParameters(*(params))
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Setting Connection Location Names

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We are also going to define specific station IDs so that our plot is not -too cluttered.

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# Define a list of station IDs to plot
-selected = ['KPDX', 'KOKC', 'KICT', 'KGLD', 'KMEM', 'KBOS', 'KMIA', 'KMOB', 'KABQ', 'KPHX', 'KTTF',
-            'KORD', 'KBIL', 'KBIS', 'KCPR', 'KLAX', 'KATL', 'KMSP', 'KSLC', 'KDFW', 'KNYC', 'KPHL',
-            'KPIT', 'KIND', 'KOLY', 'KSYR', 'KLEX', 'KCHS', 'KTLH', 'KHOU', 'KGJT', 'KLBB', 'KLSV',
-            'KGRB', 'KCLT', 'KLNK', 'KDSM', 'KBOI', 'KFSD', 'KRAP', 'KRIC', 'KJAN', 'KHSV', 'KCRW',
-            'KSAT', 'KBUY', 'K0CO', 'KZPC', 'KVIH', 'KBDG', 'KMLF', 'KELY', 'KWMC', 'KOTH', 'KCAR',
-            'KLMT', 'KRDM', 'KPDT', 'KSEA', 'KUIL', 'KEPH', 'KPUW', 'KCOE', 'KMLP', 'KPIH', 'KIDA',
-            'KMSO', 'KACV', 'KHLN', 'KBIL', 'KOLF', 'KRUT', 'KPSM', 'KJAX', 'KTPA', 'KSHV', 'KMSY',
-            'KELP', 'KRNO', 'KFAT', 'KSFO', 'KNYL', 'KBRO', 'KMRF', 'KDRT', 'KFAR', 'KBDE', 'KDLH',
-            'KHOT', 'KLBF', 'KFLG', 'KCLE', 'KUNV']
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-# set the location names to the desired station IDs
-request.setLocationNames(*(selected))
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Filter by Time

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Here we decide how much data we want to pull from EDEX. By default we’ll -request 1 hour, but that value can easily be modified by adjusting the -``timedelta(hours = 1)` <https://docs.python.org/3/library/datetime.html#timedelta-objects>`__ -in line 2. The more data we request, the longer this section will -take to run.

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# Time range
-lastHourDateTime = datetime.utcnow() - timedelta(hours = 1)
-start = lastHourDateTime.strftime('%Y-%m-%d %H')
-beginRange = datetime.strptime( start + ":00:00", "%Y-%m-%d %H:%M:%S")
-endRange = datetime.strptime( start + ":59:59", "%Y-%m-%d %H:%M:%S")
-timerange = TimeRange(beginRange, endRange)
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Use the Data!

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Get the Data!

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Now that we have our request and TimeRange timerange objects -ready, we’re ready to get the data array from EDEX.

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# Get response
-response = DataAccessLayer.getGeometryData(request,timerange)
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Extract all Parameters

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In this section we start gathering all the information we’ll need to -properly display our data. First we create an empty dictionary and array -to keep track of all data and unique station IDs. We also create a -boolean to help us only grab the first entry for skyCover related to -a station id.

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<b>Note:</b>  The way the data responses are returned, we recieve many <code>skyCover</code> entries for each station ID, but we only want to keep track of the most recent one (first one returned).
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After defining these variables, we are ready to start looping through -our response data. If the response is an entry of skyCover, and this -is a new station id, then set the skyCover value in the obs dictionary. -If this is not a skyCover entry, then explicitly set the timeObs -variable (because we have to manipulate it slightly), and dynamically -set all the remaining parameters.

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# define a dictionary and array that will be populated from our for loop below
-obs = dict({params: [] for params in params})
-station_names = []
-time_title = ""
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-# cycle through all the data in the response, in reverse order to get the most recent data first
-for ob in reversed(response):
-    avail_params = ob.getParameters()
-    #print(avail_params)
-    # if it has cloud information, we want the last of the 6 entries (most recent)
-    if "skyCover" in avail_params:
-        if i == 5:
-            # store the associated cloud cover int for the skyCover string
-            obs['skyCover'].append(get_cloud_cover(ob.getString("skyCover")))
-        i = i + 1
-    elif "stationName" in avail_params:
-        # If we already have a record for this stationName, skip
-        if ob.getString('stationName') not in station_names:
-            station_names.append(ob.getString('stationName'))
-            i = 0
-            for param in single_value_params:
-                if param in avail_params:
-                    if param == 'timeObs':
-                        obs[param].append(datetime.fromtimestamp(ob.getNumber(param)/1000.0))
-                        if time_title == "":
-                            time_title = str(ob.getDataTime())
-                    else:
-                        try:
-                            obs[param].append(ob.getNumber(param))
-                        except TypeError:
-                            obs[param].append(ob.getString(param))
-                else:
-                    obs[param].append(None)
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Populate the Data Dictionary

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Next grab the variables out of the obs dictionary we just populated, -attach correct units, (calculate their components, in the instance of -wind) and put them into a new dictionary that we will hand the plotting -function later.

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data = dict()
-data['stid'] = np.array(obs['stationName'])
-data['latitude']  = np.array(obs['latitude'])
-data['longitude'] = np.array(obs['longitude'])
-data['air_temperature'] = np.array(obs['temperature'], dtype=float)* units.degC
-data['dew_point_temperature'] = np.array(obs['dewpoint'], dtype=float)* units.degC
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-direction = np.array(obs['windDir'])
-direction[direction == -9999.0] = 'nan'
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-u, v = wind_components(np.array(obs['windSpeed']) * units('knots'),
-                           direction * units.degree)
-data['eastward_wind'], data['northward_wind'] = u, v
-data['cloud_coverage'] = np.array(obs['skyCover'])
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Plot the Data!

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Now we have all the data we need to create our plot! First we’ll assign -a projection and create our figure and axes.

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Next, we use Cartopy to add common features (land, ocean, lakes, -borders, etc) to help give us a more contextual map of the United States -to plot the METAR stations on. We create and add a title for our figure -as well.

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Additionally, we use MetPy’s -StationPlotLayout -to instantiate a custom layout and define all the attributes we want -displayed. We need to then set the data dictionary (containing all of -our data values) on the custom layout so it knows what to draw.

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Finally, we display the plot!

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proj = ccrs.LambertConformal(central_longitude=-95, central_latitude=35,
-                             standard_parallels=[35])
-# Create the figure
-fig = plt.figure(figsize=(20, 10))
-ax = fig.add_subplot(1, 1, 1, projection=proj)
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-# Add various map elements
-ax.add_feature(cfeature.LAND)
-ax.add_feature(cfeature.OCEAN)
-ax.add_feature(cfeature.LAKES)
-ax.add_feature(cfeature.COASTLINE)
-ax.add_feature(cfeature.STATES)
-ax.add_feature(cfeature.BORDERS, linewidth=2)
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-# Set plot bounds
-ax.set_extent((-118, -73, 23, 50))
-ax.set_title(time_title + " | METAR | " + edexServer)
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-# Winds, temps, dewpoint, station id
-custom_layout = StationPlotLayout()
-custom_layout.add_barb('eastward_wind', 'northward_wind', units='knots')
-custom_layout.add_value('NW', 'air_temperature', fmt='.0f', units='degF', color='darkred')
-custom_layout.add_value('SW', 'dew_point_temperature', fmt='.0f', units='degF', color='darkgreen')
-custom_layout.add_symbol('C', 'cloud_coverage', sky_cover)
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-stationplot = StationPlot(ax, data['longitude'], data['latitude'], clip_on=True,
-                          transform=ccrs.PlateCarree(), fontsize=10)
-stationplot.plot_text((2, 0), data['stid'])
-custom_layout.plot(stationplot, data)
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-plt.show()
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-../../_images/METAR_main_25_0.png -

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See Also

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Additional Documentation

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python-awips:

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datetime:

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numpy:

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cartopy:

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matplotlib:

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metpy:

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