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664 lines
17 KiB
ReStructuredText
664 lines
17 KiB
ReStructuredText
=================
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Satellite Imagery
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=================
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`Notebook <http://nbviewer.ipython.org/github/Unidata/python-awips/blob/master/examples/notebooks/Satellite_Imagery.ipynb>`_
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Satellite images are returned by Python AWIPS as grids, and can be
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rendered with Cartopy pcolormesh the same as gridded forecast models in
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other python-awips examples.
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Available Sources, Creating Entities, Sectors, and Products
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. code:: ipython3
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from awips.dataaccess import DataAccessLayer
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import cartopy.crs as ccrs
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import cartopy.feature as cfeat
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import matplotlib.pyplot as plt
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from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
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import numpy as np
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import datetime
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# Create an EDEX data request
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DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu")
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request = DataAccessLayer.newDataRequest()
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request.setDatatype("satellite")
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# get optional identifiers for satellite datatype
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identifiers = set(DataAccessLayer.getOptionalIdentifiers(request))
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print("Available Identifiers:")
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for id in identifiers:
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if id.lower() == 'datauri':
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continue
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print(" - " + id)
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.. parsed-literal::
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Available Identifiers:
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- physicalElement
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- creatingEntity
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- source
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- sectorID
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.. code:: ipython3
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# Show available sources
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identifier = "source"
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sources = DataAccessLayer.getIdentifierValues(request, identifier)
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print(identifier + ":")
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print(list(sources))
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.. parsed-literal::
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source:
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['NESDIS', 'WCDAS', 'NSOF', 'UCAR', 'McIDAS']
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.. code:: ipython3
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# Show available creatingEntities
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identifier = "creatingEntity"
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creatingEntities = DataAccessLayer.getIdentifierValues(request, identifier)
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print(identifier + ":")
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print(list(creatingEntities))
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.. parsed-literal::
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creatingEntity:
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['GOES-16', 'Composite', 'GOES-15(P)', 'POES-NPOESS', 'UNIWISC', 'GOES-11(L)', 'Miscellaneous', 'GOES-17', 'NEXRCOMP']
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.. code:: ipython3
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# Show available sectorIDs
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identifier = "sectorID"
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sectorIDs = DataAccessLayer.getIdentifierValues(request, identifier)
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print(identifier + ":")
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print(list(sectorIDs))
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.. parsed-literal::
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sectorID:
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['EMESO-2', 'Northern Hemisphere Composite', 'EFD', 'TCONUS', 'Arctic', 'TFD', 'PRREGI', 'GOES-Sounder', 'EMESO-1', 'NEXRCOMP', 'ECONUS', 'GOES-West', 'Antarctic', 'GOES-East', 'Supernational', 'West CONUS', 'NH Composite - Meteosat-GOES E-GOES W-GMS']
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.. code:: ipython3
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# Contrust a full satellite product tree
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for entity in creatingEntities:
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print(entity)
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request = DataAccessLayer.newDataRequest("satellite")
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request.addIdentifier("creatingEntity", entity)
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availableSectors = DataAccessLayer.getAvailableLocationNames(request)
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availableSectors.sort()
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for sector in availableSectors:
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print(" - " + sector)
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request.setLocationNames(sector)
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availableProducts = DataAccessLayer.getAvailableParameters(request)
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availableProducts.sort()
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for product in availableProducts:
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print(" - " + product)
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.. parsed-literal::
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GOES-16
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- ECONUS
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- ACTP
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- ADP
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- AOD
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- CAPE
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- CH-01-0.47um
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- CH-02-0.64um
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- CH-03-0.87um
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- CH-04-1.38um
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- CH-05-1.61um
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- CH-06-2.25um
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- CH-07-3.90um
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- CH-08-6.19um
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- CH-09-6.95um
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- CH-10-7.34um
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- CH-11-8.50um
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- CH-12-9.61um
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- CH-13-10.35um
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- CH-14-11.20um
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- CH-15-12.30um
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- CH-16-13.30um
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- CSM
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- CTH
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- FDC Area
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- FDC Power
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- FDC Temp
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- KI
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- LI
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- LST
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- SI
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- TPW
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- TT
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- VMP-0.00hPa
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- VMP-0.02hPa
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- VMP-0.04hPa
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- VMP-0.08hPa
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- VMP-0.14hPa
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- VMP-0.22hPa
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- VMP-0.35hPa
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- VMP-0.51hPa
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- VMP-0.71hPa
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- VMP-0.98hPa
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- VMP-1.30hPa
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- VMP-1.69hPa
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- VMP-1013.95hPa
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- VMP-103.02hPa
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- VMP-1042.23hPa
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- VMP-1070.92hPa
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- VMP-11.00hPa
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- VMP-110.24hPa
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- VMP-1100.00hPa
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- VMP-117.78hPa
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- VMP-12.65hPa
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- VMP-125.65hPa
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- VMP-133.85hPa
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- VMP-14.46hPa
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- VMP-142.38hPa
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- VMP-151.27hPa
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- VMP-16.43hPa
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- VMP-160.50hPa
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- VMP-170.08hPa
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- VMP-18.58hPa
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- VMP-180.02hPa
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- VMP-190.32hPa
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- VMP-2.15hPa
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- VMP-2.70hPa
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- VMP-20.92hPa
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- VMP-200.99hPa
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- VMP-212.03hPa
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- VMP-223.44hPa
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- VMP-23.45hPa
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- VMP-235.23hPa
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- VMP-247.41hPa
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- VMP-259.97hPa
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- VMP-26.18hPa
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- VMP-272.92hPa
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- VMP-286.26hPa
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- VMP-29.12hPa
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- VMP-3.34hPa
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- VMP-300.00hPa
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- VMP-314.14hPa
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- VMP-32.27hPa
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- VMP-328.68hPa
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- VMP-343.62hPa
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- VMP-35.65hPa
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- VMP-358.97hPa
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- VMP-374.72hPa
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- VMP-39.26hPa
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- VMP-390.89hPa
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- VMP-4.08hPa
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- VMP-4.92hPa
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- VMP-407.47hPa
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- VMP-424.47hPa
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- VMP-43.10hPa
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- VMP-441.88hPa
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- VMP-459.71hPa
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- VMP-47.19hPa
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- VMP-477.96hPa
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- VMP-496.63hPa
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- VMP-5.88hPa
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- VMP-51.53hPa
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- VMP-515.72hPa
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- VMP-535.23hPa
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- VMP-555.17hPa
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- VMP-56.13hPa
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- VMP-575.52hPa
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- VMP-596.31hPa
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- VMP-6.96hPa
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- VMP-60.99hPa
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- VMP-617.51hPa
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- VMP-639.14hPa
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- VMP-66.13hPa
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- VMP-661.19hPa
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- VMP-683.67hPa
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- VMP-706.57hPa
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- VMP-71.54hPa
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- VMP-729.89hPa
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- VMP-753.63hPa
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- VMP-77.24hPa
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- VMP-777.79hPa
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- VMP-8.17hPa
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- VMP-802.37hPa
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- VMP-827.37hPa
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- VMP-83.23hPa
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- VMP-852.79hPa
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- VMP-878.62hPa
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- VMP-89.52hPa
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- VMP-9.51hPa
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- VMP-904.87hPa
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- VMP-931.52hPa
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- VMP-958.59hPa
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- VMP-96.11hPa
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- VMP-986.07hPa
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- VTP-0.00hPa
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- VTP-0.02hPa
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- VTP-0.04hPa
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- VTP-0.08hPa
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- VTP-0.14hPa
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- VTP-0.22hPa
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- VTP-0.35hPa
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- VTP-0.51hPa
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- VTP-0.71hPa
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- VTP-0.98hPa
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- VTP-1.30hPa
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- VTP-1.69hPa
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- VTP-1013.95hPa
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- VTP-103.02hPa
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- VTP-1042.23hPa
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- VTP-1070.92hPa
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- VTP-11.00hPa
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- VTP-110.24hPa
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- VTP-1100.00hPa
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- VTP-117.78hPa
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- VTP-12.65hPa
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- VTP-125.65hPa
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- VTP-133.85hPa
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- VTP-14.46hPa
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- VTP-142.38hPa
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- VTP-151.27hPa
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- VTP-16.43hPa
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- VTP-160.50hPa
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- VTP-170.08hPa
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- VTP-18.58hPa
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- VTP-180.02hPa
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- VTP-190.32hPa
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- VTP-2.15hPa
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- VTP-2.70hPa
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- VTP-20.92hPa
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- VTP-200.99hPa
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- VTP-212.03hPa
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- VTP-223.44hPa
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- VTP-23.45hPa
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- VTP-235.23hPa
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- VTP-247.41hPa
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- VTP-259.97hPa
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- VTP-26.18hPa
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- VTP-272.92hPa
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- VTP-286.26hPa
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- VTP-29.12hPa
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- VTP-3.34hPa
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- VTP-300.00hPa
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- VTP-314.14hPa
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- VTP-32.27hPa
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- VTP-328.68hPa
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- VTP-343.62hPa
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- VTP-35.65hPa
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- VTP-358.97hPa
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- VTP-374.72hPa
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- VTP-39.26hPa
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- VTP-390.89hPa
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- VTP-4.08hPa
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- VTP-4.92hPa
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- VTP-407.47hPa
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- VTP-424.47hPa
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- VTP-43.10hPa
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- VTP-441.88hPa
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- VTP-459.71hPa
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- VTP-47.19hPa
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- VTP-477.96hPa
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- VTP-496.63hPa
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- VTP-5.88hPa
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- VTP-51.53hPa
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- VTP-515.72hPa
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- VTP-535.23hPa
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- VTP-555.17hPa
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- VTP-56.13hPa
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- VTP-575.52hPa
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- VTP-596.31hPa
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- VTP-6.96hPa
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- VTP-60.99hPa
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- VTP-617.51hPa
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- VTP-639.14hPa
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- VTP-66.13hPa
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- VTP-661.19hPa
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- VTP-683.67hPa
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- VTP-706.57hPa
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- VTP-71.54hPa
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- VTP-729.89hPa
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- VTP-753.63hPa
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- VTP-77.24hPa
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- VTP-777.79hPa
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- VTP-8.17hPa
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- VTP-802.37hPa
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- VTP-827.37hPa
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- VTP-83.23hPa
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- VTP-852.79hPa
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- VTP-878.62hPa
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- VTP-89.52hPa
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- VTP-9.51hPa
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- VTP-904.87hPa
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- VTP-931.52hPa
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- VTP-958.59hPa
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- VTP-96.11hPa
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- VTP-986.07hPa
|
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- EFD
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- ACTP
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- ADP
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- AOD
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- CAPE
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- CH-01-0.47um
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- CH-02-0.64um
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- CH-03-0.87um
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- CH-04-1.38um
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|
- CH-05-1.61um
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|
- CH-06-2.25um
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|
- CH-07-3.90um
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|
- CH-08-6.19um
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- CH-09-6.95um
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- CH-10-7.34um
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|
- CH-11-8.50um
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- CH-12-9.61um
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- CH-13-10.35um
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- CH-14-11.20um
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- CH-15-12.30um
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- CH-16-13.30um
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- CSM
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- CTH
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- CTT
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- FDC Area
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- FDC Power
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- FDC Temp
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- KI
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- LI
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- LST
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- RRQPE
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- SI
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- SST
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- TPW
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- TT
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- VAH
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- VAML
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- EMESO-1
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- ACTP
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- ADP
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- CAPE
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- CH-01-0.47um
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- CH-02-0.64um
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- CH-03-0.87um
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- CH-04-1.38um
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- CH-05-1.61um
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|
- CH-06-2.25um
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|
- CH-07-3.90um
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|
- CH-08-6.19um
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- CH-09-6.95um
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- CH-10-7.34um
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|
- CH-11-8.50um
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|
- CH-12-9.61um
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|
- CH-13-10.35um
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- CH-14-11.20um
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- CH-15-12.30um
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- CH-16-13.30um
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- CSM
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- CTH
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- CTT
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- KI
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- LI
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- LST
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- SI
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|
- TPW
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- TT
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- EMESO-2
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|
- ACTP
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- ADP
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- CAPE
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|
- CH-01-0.47um
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|
- CH-02-0.64um
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|
- CH-03-0.87um
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|
- CH-04-1.38um
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|
- CH-05-1.61um
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|
- CH-06-2.25um
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|
- CH-07-3.90um
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|
- CH-08-6.19um
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|
- CH-09-6.95um
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|
- CH-10-7.34um
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|
- CH-11-8.50um
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|
- CH-12-9.61um
|
|
- CH-13-10.35um
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|
- CH-14-11.20um
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|
- CH-15-12.30um
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|
- CH-16-13.30um
|
|
- CSM
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|
- CTH
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|
- CTT
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|
- KI
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|
- LI
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|
- LST
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|
- SI
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|
- TPW
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|
- TT
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|
- PRREGI
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|
- CH-01-0.47um
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|
- CH-02-0.64um
|
|
- CH-03-0.87um
|
|
- CH-04-1.38um
|
|
- CH-05-1.61um
|
|
- CH-06-2.25um
|
|
- CH-07-3.90um
|
|
- CH-08-6.19um
|
|
- CH-09-6.95um
|
|
- CH-10-7.34um
|
|
- CH-11-8.50um
|
|
- CH-12-9.61um
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|
- CH-13-10.35um
|
|
- CH-14-11.20um
|
|
- CH-15-12.30um
|
|
- CH-16-13.30um
|
|
Composite
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|
- NH Composite - Meteosat-GOES E-GOES W-GMS
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- Imager 11 micron IR
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|
- Imager 6.7-6.5 micron IR (WV)
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|
- Imager Visible
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|
- Supernational
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|
- Gridded Cloud Amount
|
|
- Gridded Cloud Top Pressure or Height
|
|
- Sounder Based Derived Lifted Index (LI)
|
|
- Sounder Based Derived Precipitable Water (PW)
|
|
- Sounder Based Derived Surface Skin Temp (SFC Skin)
|
|
GOES-15(P)
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|
- Northern Hemisphere Composite
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|
- Imager 11 micron IR
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|
- Imager 6.7-6.5 micron IR (WV)
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|
- Imager Visible
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|
- Supernational
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|
- Imager 11 micron IR
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|
- Imager 6.7-6.5 micron IR (WV)
|
|
- Imager Visible
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|
- West CONUS
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|
- Imager 11 micron IR
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|
- Imager 13 micron IR
|
|
- Imager 3.9 micron IR
|
|
- Imager 6.7-6.5 micron IR (WV)
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|
- Imager Visible
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|
- Sounder 11.03 micron imagery
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|
- Sounder 14.06 micron imagery
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|
- Sounder 3.98 micron imagery
|
|
- Sounder 4.45 micron imagery
|
|
- Sounder 6.51 micron imagery
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|
- Sounder 7.02 micron imagery
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|
- Sounder 7.43 micron imagery
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|
- Sounder Visible imagery
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|
POES-NPOESS
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- Supernational
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|
- Rain fall rate
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|
UNIWISC
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- Antarctic
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|
- Imager 11 micron IR
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|
- Imager 12 micron IR
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|
- Imager 3.5-4.0 micron IR (Fog)
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|
- Imager 6.7-6.5 micron IR (WV)
|
|
- Imager Visible
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|
- Arctic
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|
- Imager 11 micron IR
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|
- Imager 12 micron IR
|
|
- Imager 3.5-4.0 micron IR (Fog)
|
|
- Imager 6.7-6.5 micron IR (WV)
|
|
- Imager Visible
|
|
- GOES-East
|
|
- Imager 11 micron IR
|
|
- Imager 13 micron IR
|
|
- Imager 3.5-4.0 micron IR (Fog)
|
|
- Imager 6.7-6.5 micron IR (WV)
|
|
- Imager Visible
|
|
- GOES-Sounder
|
|
- CAPE
|
|
- Sounder Based Derived Lifted Index (LI)
|
|
- Sounder Based Derived Precipitable Water (PW)
|
|
- Sounder Based Total Column Ozone
|
|
- GOES-West
|
|
- Imager 11 micron IR
|
|
- Imager 13 micron IR
|
|
- Imager 3.5-4.0 micron IR (Fog)
|
|
- Imager 6.7-6.5 micron IR (WV)
|
|
- Imager Visible
|
|
GOES-11(L)
|
|
- West CONUS
|
|
- Low cloud base imagery
|
|
Miscellaneous
|
|
- Supernational
|
|
- Percent of Normal TPW
|
|
- Sounder Based Derived Precipitable Water (PW)
|
|
GOES-17
|
|
- TCONUS
|
|
- CH-01-0.47um
|
|
- CH-02-0.64um
|
|
- CH-03-0.87um
|
|
- CH-04-1.38um
|
|
- CH-05-1.61um
|
|
- CH-06-2.25um
|
|
- CH-07-3.90um
|
|
- CH-08-6.19um
|
|
- CH-09-6.95um
|
|
- CH-10-7.34um
|
|
- CH-11-8.50um
|
|
- CH-12-9.61um
|
|
- CH-13-10.35um
|
|
- CH-14-11.20um
|
|
- CH-15-12.30um
|
|
- CH-16-13.30um
|
|
- TFD
|
|
- CH-01-0.47um
|
|
- CH-02-0.64um
|
|
- CH-03-0.87um
|
|
- CH-04-1.38um
|
|
- CH-05-1.61um
|
|
- CH-06-2.25um
|
|
- CH-07-3.90um
|
|
- CH-08-6.19um
|
|
- CH-09-6.95um
|
|
- CH-10-7.34um
|
|
- CH-11-8.50um
|
|
- CH-12-9.61um
|
|
- CH-13-10.35um
|
|
- CH-14-11.20um
|
|
- CH-15-12.30um
|
|
- CH-16-13.30um
|
|
NEXRCOMP
|
|
- NEXRCOMP
|
|
- DHR
|
|
- DVL
|
|
- EET
|
|
- HHC
|
|
- N0R
|
|
- N1P
|
|
- NTP
|
|
|
|
|
|
GOES 16 Mesoscale Sectors
|
|
-------------------------
|
|
|
|
Define our imports, and define our map properties first.
|
|
|
|
.. code:: ipython3
|
|
|
|
%matplotlib inline
|
|
|
|
def make_map(bbox, projection=ccrs.PlateCarree()):
|
|
fig, ax = plt.subplots(figsize=(10,12),
|
|
subplot_kw=dict(projection=projection))
|
|
if bbox[0] is not np.nan:
|
|
ax.set_extent(bbox)
|
|
ax.coastlines(resolution='50m')
|
|
gl = ax.gridlines(draw_labels=True)
|
|
gl.top_labels = gl.right_labels = False
|
|
gl.xformatter = LONGITUDE_FORMATTER
|
|
gl.yformatter = LATITUDE_FORMATTER
|
|
return fig, ax
|
|
|
|
sectors = ["EMESO-1","EMESO-2"]
|
|
fig = plt.figure(figsize=(16,7*len(sectors)))
|
|
|
|
for i, sector in enumerate(sectors):
|
|
|
|
request = DataAccessLayer.newDataRequest()
|
|
request.setDatatype("satellite")
|
|
request.setLocationNames(sector)
|
|
request.setParameters("CH-13-10.35um")
|
|
|
|
utc = datetime.datetime.utcnow()
|
|
times = DataAccessLayer.getAvailableTimes(request)
|
|
hourdiff = utc - datetime.datetime.strptime(str(times[-1]),'%Y-%m-%d %H:%M:%S')
|
|
hours,days = hourdiff.seconds/3600,hourdiff.days
|
|
minute = str((hourdiff.seconds - (3600 * hours)) / 60)
|
|
offsetStr = ''
|
|
if hours > 0:
|
|
offsetStr += str(hours) + "hr "
|
|
offsetStr += str(minute) + "m ago"
|
|
if days > 1:
|
|
offsetStr = str(days) + " days ago"
|
|
|
|
response = DataAccessLayer.getGridData(request, [times[-1]])
|
|
grid = response[0]
|
|
data = grid.getRawData()
|
|
lons,lats = grid.getLatLonCoords()
|
|
bbox = [lons.min(), lons.max(), lats.min(), lats.max()]
|
|
|
|
print("Latest image available: "+str(times[-1]) + " ("+offsetStr+")")
|
|
print("Image grid size: " + str(data.shape))
|
|
print("Image grid extent: " + str(list(bbox)))
|
|
|
|
fig, ax = make_map(bbox=bbox)
|
|
states = cfeat.NaturalEarthFeature(category='cultural',
|
|
name='admin_1_states_provinces_lines',
|
|
scale='50m', facecolor='none')
|
|
ax.add_feature(states, linestyle=':')
|
|
cs = ax.pcolormesh(lons, lats, data, cmap='coolwarm')
|
|
cbar = fig.colorbar(cs, shrink=0.6, orientation='horizontal')
|
|
cbar.set_label(sector + " " + grid.getParameter() + " " \
|
|
+ str(grid.getDataTime().getRefTime()))
|
|
|
|
|
|
.. parsed-literal::
|
|
|
|
Latest image available: 2018-10-09 19:17:28 (0.021388888888888888hr 0.0m ago)
|
|
Image grid size: (500, 500)
|
|
Image grid extent: [-92.47462, -80.657455, 20.24799, 31.116167]
|
|
Latest image available: 2018-10-09 14:30:58 (4.797777777777778hr 0.0m ago)
|
|
Image grid size: (500, 500)
|
|
Image grid extent: [-104.61595, -87.45227, 29.422266, 42.70851]
|
|
|
|
|
|
|
|
.. parsed-literal::
|
|
|
|
<Figure size 1152x1008 with 0 Axes>
|
|
|
|
|
|
|
|
.. image:: Satellite_Imagery_files/Satellite_Imagery_7_2.png
|
|
|
|
|
|
|
|
.. image:: Satellite_Imagery_files/Satellite_Imagery_7_3.png
|
|
|