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Satellite Imagery
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2018-01-23 11:45:59 -07:00
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2017-08-14 10:18:50 -07:00
< h1 > Satellite Imagery< / h1 >
< p > 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. < / p >
< pre > < code class = "python" > %matplotlib inline
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
DataAccessLayer.changeEDEXHost(" edex-cloud.unidata.ucar.edu" )
request = DataAccessLayer.newDataRequest()
request.setDatatype(" satellite" )
< / code > < / pre >
< h3 id = "available-satellite-sectors-and-products" > Available Satellite Sectors and Products< a class = "headerlink" href = "#available-satellite-sectors-and-products" title = "Permanent link" > < / a > < / h3 >
< pre > < code class = "python" > availableSectors = DataAccessLayer.getAvailableLocationNames(request)
availableSectors.sort()
print(" \nAvailable sectors and products\n" )
for sect in availableSectors:
request.setLocationNames(sect)
availableProducts = DataAccessLayer.getAvailableParameters(request)
availableProducts.sort()
print(sect + " :" )
for prod in availableProducts:
print(" - " +prod)
< / code > < / pre >
< p > Available sectors and products< / p >
< ul >
< li > Alaska National:< ul >
< li > Imager 11 micron IR< / li >
< li > Imager 6.7-6.5 micron IR (WV)< / li >
< li > Imager Visible< / li >
< li > Percent of Normal TPW< / li >
< li > Rain fall rate< / li >
< li > Sounder Based Derived Precipitable Water (PW)< / li >
< / ul >
< / li >
< li > Alaska Regional:< ul >
< li > Imager 11 micron IR< / li >
< li > Imager 3.9 micron IR< / li >
< li > Imager 6.7-6.5 micron IR (WV)< / li >
< li > Imager Visible< / li >
< / ul >
< / li >
< li > East CONUS:< ul >
< li > Imager 11 micron IR< / li >
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< li > Imager 13 micron IR< / li >
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< li > Imager 3.9 micron IR< / li >
< li > Imager 6.7-6.5 micron IR (WV)< / li >
< li > Imager Visible< / li >
< li > Low cloud base imagery< / li >
< / ul >
< / li >
< li > GOES-East:< ul >
< li > Imager 11 micron IR< / li >
< li > Imager 13 micron IR< / li >
< li > Imager 3.5-4.0 micron IR (Fog)< / li >
< li > Imager 6.7-6.5 micron IR (WV)< / li >
< li > Imager Visible< / li >
< / ul >
< / li >
< li > GOES-East-West:< ul >
< li > Imager 11 micron IR< / li >
< li > Imager 13 micron IR< / li >
< li > Imager 3.5-4.0 micron IR (Fog)< / li >
< li > Imager 6.7-6.5 micron IR (WV)< / li >
< li > Imager Visible< / li >
< / ul >
< / li >
< li > GOES-Sounder:< ul >
< li > CAPE< / li >
< li > Sounder Based Derived Lifted Index (LI)< / li >
< li > Sounder Based Derived Precipitable Water (PW)< / li >
< li > Sounder Based Derived Surface Skin Temp (SFC Skin)< / li >
< li > Sounder Based Total Column Ozone< / li >
< / ul >
< / li >
< li > GOES-West:< ul >
< li > Imager 11 micron IR< / li >
< li > Imager 13 micron IR< / li >
< li > Imager 3.5-4.0 micron IR (Fog)< / li >
< li > Imager 6.7-6.5 micron IR (WV)< / li >
< li > Imager Visible< / li >
< / ul >
< / li >
< li > Global:< ul >
< li > Imager 11 micron IR< / li >
< li > Imager 6.7-6.5 micron IR (WV)< / li >
< / ul >
< / li >
< li > Hawaii National:< ul >
< li > Gridded Cloud Amount< / li >
< li > Gridded Cloud Top Pressure or Height< / li >
< li > Imager 11 micron IR< / li >
< li > Imager 6.7-6.5 micron IR (WV)< / li >
< li > Imager Visible< / li >
< li > Percent of Normal TPW< / li >
< li > Rain fall rate< / li >
< li > Sounder 11.03 micron imagery< / li >
< li > Sounder 14.06 micron imagery< / li >
< li > Sounder 3.98 micron imagery< / li >
< li > Sounder 4.45 micron imagery< / li >
< li > Sounder 6.51 micron imagery< / li >
< li > Sounder 7.02 micron imagery< / li >
< li > Sounder 7.43 micron imagery< / li >
< li > Sounder Based Derived Lifted Index (LI)< / li >
< li > Sounder Based Derived Precipitable Water (PW)< / li >
< li > Sounder Based Derived Surface Skin Temp (SFC Skin)< / li >
< li > Sounder Visible imagery< / li >
< / ul >
< / li >
< li > Hawaii Regional:< ul >
< li > Imager 11 micron IR< / li >
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< li > Imager 13 micron IR< / li >
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< li > Imager 3.9 micron IR< / li >
< li > Imager 6.7-6.5 micron IR (WV)< / li >
< li > Imager Visible< / li >
< / ul >
< / li >
< li > Mollweide:< ul >
< li > Imager 11 micron IR< / li >
< li > Imager 6.7-6.5 micron IR (WV)< / li >
< / ul >
< / li >
< li > NEXRCOMP:< ul >
< li > DHR< / li >
< li > DVL< / li >
< li > EET< / li >
< li > HHC< / li >
< li > N0R< / li >
< li > N1P< / li >
< li > NTP< / li >
< / ul >
< / li >
< li > NH Composite - Meteosat-GOES E-GOES W-GMS:< ul >
< li > Imager 11 micron IR< / li >
< li > Imager 6.7-6.5 micron IR (WV)< / li >
< li > Imager Visible< / li >
< / ul >
< / li >
< li > Northern Hemisphere Composite:< ul >
< li > Imager 11 micron IR< / li >
< li > Imager 6.7-6.5 micron IR (WV)< / li >
< li > Imager Visible< / li >
< / ul >
< / li >
< li > Puerto Rico National:< ul >
< li > Imager 11 micron IR< / li >
< li > Imager 6.7-6.5 micron IR (WV)< / li >
< li > Imager Visible< / li >
< li > Percent of Normal TPW< / li >
< li > Rain fall rate< / li >
< li > Sounder Based Derived Precipitable Water (PW)< / li >
< / ul >
< / li >
< li > Puerto Rico Regional:< ul >
< li > Imager 11 micron IR< / li >
2018-01-23 11:45:59 -07:00
< li > Imager 13 micron IR< / li >
2017-08-14 10:18:50 -07:00
< li > Imager 3.9 micron IR< / li >
< li > Imager 6.7-6.5 micron IR (WV)< / li >
< li > Imager Visible< / li >
< / ul >
< / li >
< li > Supernational:< ul >
< li > Gridded Cloud Amount< / li >
< li > Gridded Cloud Top Pressure or Height< / li >
< li > Imager 11 micron IR< / li >
< li > Imager 6.7-6.5 micron IR (WV)< / li >
< li > Imager Visible< / li >
< li > Percent of Normal TPW< / li >
< li > Rain fall rate< / li >
< li > Sounder Based Derived Lifted Index (LI)< / li >
< li > Sounder Based Derived Precipitable Water (PW)< / li >
< li > Sounder Based Derived Surface Skin Temp (SFC Skin)< / li >
< / ul >
< / li >
< li > West CONUS:< ul >
< li > Imager 11 micron IR< / li >
2018-01-23 11:45:59 -07:00
< li > Imager 13 micron IR< / li >
2017-08-14 10:18:50 -07:00
< li > Imager 3.9 micron IR< / li >
< li > Imager 6.7-6.5 micron IR (WV)< / li >
< li > Imager Visible< / li >
< li > Low cloud base imagery< / li >
< li > Sounder 11.03 micron imagery< / li >
< li > Sounder 14.06 micron imagery< / li >
< li > Sounder 3.98 micron imagery< / li >
< li > Sounder 4.45 micron imagery< / li >
< li > Sounder 6.51 micron imagery< / li >
< li > Sounder 7.02 micron imagery< / li >
< li > Sounder 7.43 micron imagery< / li >
< li > Sounder Visible imagery< / li >
< / ul >
< / li >
< / ul >
< h3 id = "plot-global-water-vapor-composite" > Plot Global Water Vapor Composite< a class = "headerlink" href = "#plot-global-water-vapor-composite" title = "Permanent link" > < / a > < / h3 >
< pre > < code class = "python" > request.setLocationNames(" Global" )
availableProducts = DataAccessLayer.getAvailableParameters(request)
availableProducts.sort()
request.setParameters(availableProducts[0])
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"
print(" Found " + str(len(times)) +" available times" )
print(" " +str(times[0]) + " \n to\n " + str(times[-1]))
print(" Using " +str(times[-1]) + " (" +offsetStr+" )" )
< / code > < / pre >
< blockquote >
< pre > < code > Found 96 available times
2017-01-23 00:00:00
to
2017-02-03 21:00:00
Using 2017-02-03 21:00:00 (2hr 3m ago)
< / code > < / pre >
< / blockquote >
< pre > < code class = "python" > 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(" grid size " + str(data.shape))
print(" grid extent " + str(list(bbox)))
< / code > < / pre >
< blockquote >
< pre > < code > grid size (1024, 2048)
grid extent [-179.91191, 179.99982, -89.977936, 89.890022]
< / code > < / pre >
< / blockquote >
< pre > < code class = "python" > def make_map(bbox, projection=ccrs.PlateCarree()):
fig, ax = plt.subplots(figsize=(18,14),
subplot_kw=dict(projection=projection))
ax.set_extent(bbox)
ax.coastlines(resolution='50m')
gl = ax.gridlines(draw_labels=True)
gl.xlabels_top = gl.ylabels_right = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
return fig, ax
fig, ax = make_map(bbox=bbox)
# State boundaries
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='Greys_r')
cbar = fig.colorbar(cs, shrink=0.9, orientation='horizontal')
cbar.set_label(str(grid.getLocationName())+" " \
+str(grid.getParameter())+" " \
+str(grid.getDataTime().getRefTime()))
plt.tight_layout()
< / code > < / pre >
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