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GOES Geostationary Lightning Mapper

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Notebook -The Geostationary Lightning Mapper, or GLM, on board GOES-R Series -spacecraft, is the first operational lightning mapper flown in -geostationary orbit. GLM detects the light emitted by lightning at the -tops of clouds day and night and collects information such as the -frequency, location and extent of lightning discharges. The instrument -measures total lightning, both in-cloud and cloud-to-ground, to aid in -forecasting developing severe storms and a wide range of high-impact -environmental phenomena including hailstorms, microburst winds, -tornadoes, hurricanes, flash floods, snowstorms and fires.

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AWIPS GLM point data are available in three formats

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  • GLMev Events

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  • GLMfl Flashes

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  • GLMgr Groups

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and with seven attributes:

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  • height

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  • intensity

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  • msgType

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  • pulseCount

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  • pulseIndex

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  • sensorCount

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  • strikeType

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GLM Sources and Parameters

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from awips.dataaccess import DataAccessLayer
-import cartopy.crs as ccrs
-import cartopy.feature as cfeat
-import matplotlib.pyplot as plt
-from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
-import numpy as np
-import datetime
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-%matplotlib inline
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-# Create an EDEX data request
-edexServer = "edex-cloud.unidata.ucar.edu"
-datatype = "binlightning"
-DataAccessLayer.changeEDEXHost(edexServer)
-request = DataAccessLayer.newDataRequest(datatype)
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-# Show available sources
-sources = DataAccessLayer.getIdentifierValues(request, "source")
-print("available sources:")
-print(list(sources))
-print("")
-availableParms = DataAccessLayer.getAvailableParameters(request)
-availableParms.sort()
-print("available parameters:")
-print(list(availableParms))
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available sources:
-['GLMgr', 'GLMfl', 'GLMev']
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-available parameters:
-['height', 'intensity', 'msgType', 'pulseCount', 'pulseIndex', 'sensorCount', 'strikeType']
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request.addIdentifier("source", "GLMgr")
-request.setParameters("intensity")
-times = DataAccessLayer.getAvailableTimes(request)
-response = DataAccessLayer.getGeometryData(request, [times[-1]])
-glm_points = []
-for data in response:
-       glm_points.append(data.getGeometry())
-ob = response[0]
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# Plot markers
-fig, ax = plt.subplots(figsize=(16,16),subplot_kw=dict(projection=ccrs.Orthographic(central_longitude=-90.0)))
-ax.coastlines(resolution='50m')
-ax.gridlines()
-ax.scatter([point.x for point in glm_points],
-       [point.y for point in glm_points],
-       transform=ccrs.PlateCarree(),marker="+",facecolor='red')
-ax.set_title(str(response[-1].getDataTime().getRefTime()) + " | " + ob.getAttribute('source') + " | " + edexServer)
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Text(0.5,1,'Oct 15 18 22:15:07 GMT | GLMgr | edex-cloud.unidata.ucar.edu')
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-../../_images/GOES_Geostationary_Lightning_Mapper_3_1.png -
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