250 lines
7 KiB
Markdown
250 lines
7 KiB
Markdown
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Satellite images are returned by Python AWIPS as grids, and can be rendered with Cartopy pcolormesh the same as gridded forecast models in other python-awips examples.
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```python
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%matplotlib inline
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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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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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```
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### Available Satellite Sectors and Products
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```python
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availableSectors = DataAccessLayer.getAvailableLocationNames(request)
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availableSectors.sort()
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print("\nAvailable sectors and products\n")
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for sect in availableSectors:
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request.setLocationNames(sect)
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availableProducts = DataAccessLayer.getAvailableParameters(request)
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availableProducts.sort()
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print(sect + ":")
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for prod in availableProducts:
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print(" - "+prod)
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```
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Available sectors and products
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* Alaska National:
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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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- Percent of Normal TPW
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- Rain fall rate
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- Sounder Based Derived Precipitable Water (PW)
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* Alaska Regional:
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- Imager 11 micron IR
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- Imager 3.9 micron IR
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- Imager 6.7-6.5 micron IR (WV)
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- Imager Visible
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* East CONUS:
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- Imager 11 micron IR
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- Imager 13 micron (IR)
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- Imager 3.9 micron IR
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- Imager 6.7-6.5 micron IR (WV)
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- Imager Visible
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- Low cloud base imagery
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* GOES-East:
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- Imager 11 micron IR
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- Imager 13 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)
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- Imager Visible
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* GOES-East-West:
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- Imager 11 micron IR
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- Imager 13 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)
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- Imager Visible
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* GOES-Sounder:
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- CAPE
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- Sounder Based Derived Lifted Index (LI)
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- Sounder Based Derived Precipitable Water (PW)
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- Sounder Based Derived Surface Skin Temp (SFC Skin)
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- Sounder Based Total Column Ozone
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* GOES-West:
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- Imager 11 micron IR
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- Imager 13 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)
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- Imager Visible
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* Global:
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- Imager 11 micron IR
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- Imager 6.7-6.5 micron IR (WV)
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* Hawaii National:
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- Gridded Cloud Amount
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- Gridded Cloud Top Pressure or Height
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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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- Percent of Normal TPW
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- Rain fall rate
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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
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- Sounder 4.45 micron imagery
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- 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 Based Derived Lifted Index (LI)
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- Sounder Based Derived Precipitable Water (PW)
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- Sounder Based Derived Surface Skin Temp (SFC Skin)
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- Sounder Visible imagery
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* Hawaii Regional:
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- Imager 11 micron IR
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- Imager 13 micron (IR)
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- Imager 3.9 micron IR
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- Imager 6.7-6.5 micron IR (WV)
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- Imager Visible
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* Mollweide:
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- Imager 11 micron IR
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- Imager 6.7-6.5 micron IR (WV)
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* NEXRCOMP:
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- DHR
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- DVL
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- EET
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- HHC
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- N0R
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- N1P
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- NTP
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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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* 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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* Puerto Rico National:
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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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- Percent of Normal TPW
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- Rain fall rate
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- Sounder Based Derived Precipitable Water (PW)
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* Puerto Rico Regional:
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- Imager 11 micron IR
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- Imager 13 micron (IR)
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- Imager 3.9 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
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- Gridded Cloud Top Pressure or Height
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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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- Percent of Normal TPW
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- Rain fall rate
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- Sounder Based Derived Lifted Index (LI)
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- Sounder Based Derived Precipitable Water (PW)
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- Sounder Based Derived Surface Skin Temp (SFC Skin)
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* West CONUS:
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- Imager 11 micron IR
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- Imager 13 micron (IR)
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- Imager 3.9 micron IR
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- Imager 6.7-6.5 micron IR (WV)
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- Imager Visible
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- Low cloud base imagery
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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
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- Sounder 4.45 micron imagery
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- 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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### Plot Global Water Vapor Composite
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```python
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request.setLocationNames("Global")
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availableProducts = DataAccessLayer.getAvailableParameters(request)
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availableProducts.sort()
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request.setParameters(availableProducts[0])
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utc = datetime.datetime.utcnow()
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times = DataAccessLayer.getAvailableTimes(request)
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hourdiff = utc - datetime.datetime.strptime(str(times[-1]),'%Y-%m-%d %H:%M:%S')
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hours,days = hourdiff.seconds/3600,hourdiff.days
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minute = str((hourdiff.seconds - (3600 * hours)) / 60)
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offsetStr = ''
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if hours > 0:
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offsetStr += str(hours) + "hr "
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offsetStr += str(minute) + "m ago"
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if days > 1:
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offsetStr = str(days) + " days ago"
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print("Found "+ str(len(times)) +" available times")
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print(" "+str(times[0]) + "\n to\n " + str(times[-1]))
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print("Using "+str(times[-1]) + " ("+offsetStr+")")
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```
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> Found 96 available times
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> 2017-01-23 00:00:00
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> to
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> 2017-02-03 21:00:00
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> Using 2017-02-03 21:00:00 (2hr 3m ago)
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```python
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response = DataAccessLayer.getGridData(request, [times[-1]])
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grid = response[0]
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data = grid.getRawData()
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lons,lats = grid.getLatLonCoords()
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bbox = [lons.min(), lons.max(), lats.min(), lats.max()]
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print("grid size " + str(data.shape))
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print("grid extent " + str(list(bbox)))
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```
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> grid size (1024, 2048)
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> grid extent [-179.91191, 179.99982, -89.977936, 89.890022]
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```python
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def make_map(bbox, projection=ccrs.PlateCarree()):
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fig, ax = plt.subplots(figsize=(18,14),
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subplot_kw=dict(projection=projection))
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ax.set_extent(bbox)
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ax.coastlines(resolution='50m')
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gl = ax.gridlines(draw_labels=True)
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gl.xlabels_top = gl.ylabels_right = False
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gl.xformatter = LONGITUDE_FORMATTER
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gl.yformatter = LATITUDE_FORMATTER
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return fig, ax
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fig, ax = make_map(bbox=bbox)
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# State boundaries
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states = cfeat.NaturalEarthFeature(category='cultural',
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name='admin_1_states_provinces_lines',
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scale='50m', facecolor='none')
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ax.add_feature(states, linestyle=':')
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cs = ax.pcolormesh(lons, lats, data, cmap='Greys_r')
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cbar = fig.colorbar(cs, shrink=0.9, orientation='horizontal')
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cbar.set_label(str(grid.getLocationName())+" " \
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+str(grid.getParameter())+" " \
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+str(grid.getDataTime().getRefTime()))
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plt.tight_layout()
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```
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