Satellite Imagery
Notebook 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.
Available Sources, Creating Entities, Sectors, and Products
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
# Create an EDEX data request
DataAccessLayer.changeEDEXHost("edex-cloud.unidata.ucar.edu")
request = DataAccessLayer.newDataRequest()
request.setDatatype("satellite")
# get optional identifiers for satellite datatype
identifiers = set(DataAccessLayer.getOptionalIdentifiers(request))
print("Available Identifiers:")
for id in identifiers:
if id.lower() == 'datauri':
continue
print(" - " + id)
Available Identifiers:
- physicalElement
- creatingEntity
- source
- sectorID
# Show available sources
identifier = "source"
sources = DataAccessLayer.getIdentifierValues(request, identifier)
print(identifier + ":")
print(list(sources))
source:
['NESDIS', 'WCDAS', 'NSOF', 'UCAR', 'McIDAS']
# Show available creatingEntities
identifier = "creatingEntity"
creatingEntities = DataAccessLayer.getIdentifierValues(request, identifier)
print(identifier + ":")
print(list(creatingEntities))
creatingEntity:
['GOES-16', 'Composite', 'GOES-15(P)', 'POES-NPOESS', 'UNIWISC', 'GOES-11(L)', 'Miscellaneous', 'GOES-17', 'NEXRCOMP']
# Show available sectorIDs
identifier = "sectorID"
sectorIDs = DataAccessLayer.getIdentifierValues(request, identifier)
print(identifier + ":")
print(list(sectorIDs))
sectorID:
['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']
# Contrust a full satellite product tree
for entity in creatingEntities:
print(entity)
request = DataAccessLayer.newDataRequest("satellite")
request.addIdentifier("creatingEntity", entity)
availableSectors = DataAccessLayer.getAvailableLocationNames(request)
availableSectors.sort()
for sector in availableSectors:
print(" - " + sector)
request.setLocationNames(sector)
availableProducts = DataAccessLayer.getAvailableParameters(request)
availableProducts.sort()
for product in availableProducts:
print(" - " + product)
GOES-16
- ECONUS
- ACTP
- ADP
- AOD
- CAPE
- 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
- CSM
- CTH
- FDC Area
- FDC Power
- FDC Temp
- KI
- LI
- LST
- SI
- TPW
- TT
- VMP-0.00hPa
- VMP-0.02hPa
- VMP-0.04hPa
- VMP-0.08hPa
- VMP-0.14hPa
- VMP-0.22hPa
- VMP-0.35hPa
- VMP-0.51hPa
- VMP-0.71hPa
- VMP-0.98hPa
- VMP-1.30hPa
- VMP-1.69hPa
- VMP-1013.95hPa
- VMP-103.02hPa
- VMP-1042.23hPa
- VMP-1070.92hPa
- VMP-11.00hPa
- VMP-110.24hPa
- VMP-1100.00hPa
- VMP-117.78hPa
- VMP-12.65hPa
- VMP-125.65hPa
- VMP-133.85hPa
- VMP-14.46hPa
- VMP-142.38hPa
- VMP-151.27hPa
- VMP-16.43hPa
- VMP-160.50hPa
- VMP-170.08hPa
- VMP-18.58hPa
- VMP-180.02hPa
- VMP-190.32hPa
- VMP-2.15hPa
- VMP-2.70hPa
- VMP-20.92hPa
- VMP-200.99hPa
- VMP-212.03hPa
- VMP-223.44hPa
- VMP-23.45hPa
- VMP-235.23hPa
- VMP-247.41hPa
- VMP-259.97hPa
- VMP-26.18hPa
- VMP-272.92hPa
- VMP-286.26hPa
- VMP-29.12hPa
- VMP-3.34hPa
- VMP-300.00hPa
- VMP-314.14hPa
- VMP-32.27hPa
- VMP-328.68hPa
- VMP-343.62hPa
- VMP-35.65hPa
- VMP-358.97hPa
- VMP-374.72hPa
- VMP-39.26hPa
- VMP-390.89hPa
- VMP-4.08hPa
- VMP-4.92hPa
- VMP-407.47hPa
- VMP-424.47hPa
- VMP-43.10hPa
- VMP-441.88hPa
- VMP-459.71hPa
- VMP-47.19hPa
- VMP-477.96hPa
- VMP-496.63hPa
- VMP-5.88hPa
- VMP-51.53hPa
- VMP-515.72hPa
- VMP-535.23hPa
- VMP-555.17hPa
- VMP-56.13hPa
- VMP-575.52hPa
- VMP-596.31hPa
- VMP-6.96hPa
- VMP-60.99hPa
- VMP-617.51hPa
- VMP-639.14hPa
- VMP-66.13hPa
- VMP-661.19hPa
- VMP-683.67hPa
- VMP-706.57hPa
- VMP-71.54hPa
- VMP-729.89hPa
- VMP-753.63hPa
- VMP-77.24hPa
- VMP-777.79hPa
- VMP-8.17hPa
- VMP-802.37hPa
- VMP-827.37hPa
- VMP-83.23hPa
- VMP-852.79hPa
- VMP-878.62hPa
- VMP-89.52hPa
- VMP-9.51hPa
- VMP-904.87hPa
- VMP-931.52hPa
- VMP-958.59hPa
- VMP-96.11hPa
- VMP-986.07hPa
- VTP-0.00hPa
- VTP-0.02hPa
- VTP-0.04hPa
- VTP-0.08hPa
- VTP-0.14hPa
- VTP-0.22hPa
- VTP-0.35hPa
- VTP-0.51hPa
- VTP-0.71hPa
- VTP-0.98hPa
- VTP-1.30hPa
- VTP-1.69hPa
- VTP-1013.95hPa
- VTP-103.02hPa
- VTP-1042.23hPa
- VTP-1070.92hPa
- VTP-11.00hPa
- VTP-110.24hPa
- VTP-1100.00hPa
- VTP-117.78hPa
- VTP-12.65hPa
- VTP-125.65hPa
- VTP-133.85hPa
- VTP-14.46hPa
- VTP-142.38hPa
- VTP-151.27hPa
- VTP-16.43hPa
- VTP-160.50hPa
- VTP-170.08hPa
- VTP-18.58hPa
- VTP-180.02hPa
- VTP-190.32hPa
- VTP-2.15hPa
- VTP-2.70hPa
- VTP-20.92hPa
- VTP-200.99hPa
- VTP-212.03hPa
- VTP-223.44hPa
- VTP-23.45hPa
- VTP-235.23hPa
- VTP-247.41hPa
- VTP-259.97hPa
- VTP-26.18hPa
- VTP-272.92hPa
- VTP-286.26hPa
- VTP-29.12hPa
- VTP-3.34hPa
- VTP-300.00hPa
- VTP-314.14hPa
- VTP-32.27hPa
- VTP-328.68hPa
- VTP-343.62hPa
- VTP-35.65hPa
- VTP-358.97hPa
- VTP-374.72hPa
- VTP-39.26hPa
- VTP-390.89hPa
- VTP-4.08hPa
- VTP-4.92hPa
- VTP-407.47hPa
- VTP-424.47hPa
- VTP-43.10hPa
- VTP-441.88hPa
- VTP-459.71hPa
- VTP-47.19hPa
- VTP-477.96hPa
- VTP-496.63hPa
- VTP-5.88hPa
- VTP-51.53hPa
- VTP-515.72hPa
- VTP-535.23hPa
- VTP-555.17hPa
- VTP-56.13hPa
- VTP-575.52hPa
- VTP-596.31hPa
- VTP-6.96hPa
- VTP-60.99hPa
- VTP-617.51hPa
- VTP-639.14hPa
- VTP-66.13hPa
- VTP-661.19hPa
- VTP-683.67hPa
- VTP-706.57hPa
- VTP-71.54hPa
- VTP-729.89hPa
- VTP-753.63hPa
- VTP-77.24hPa
- VTP-777.79hPa
- VTP-8.17hPa
- VTP-802.37hPa
- VTP-827.37hPa
- VTP-83.23hPa
- VTP-852.79hPa
- VTP-878.62hPa
- VTP-89.52hPa
- VTP-9.51hPa
- VTP-904.87hPa
- VTP-931.52hPa
- VTP-958.59hPa
- VTP-96.11hPa
- VTP-986.07hPa
- EFD
- ACTP
- ADP
- AOD
- CAPE
- 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
- CSM
- CTH
- CTT
- FDC Area
- FDC Power
- FDC Temp
- KI
- LI
- LST
- RRQPE
- SI
- SST
- TPW
- TT
- VAH
- VAML
- EMESO-1
- ACTP
- ADP
- CAPE
- 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
- CSM
- CTH
- CTT
- KI
- LI
- LST
- SI
- TPW
- TT
- EMESO-2
- ACTP
- ADP
- CAPE
- 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
- CSM
- CTH
- CTT
- KI
- LI
- LST
- SI
- TPW
- TT
- PRREGI
- 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
Composite
- NH Composite - Meteosat-GOES E-GOES W-GMS
- Imager 11 micron IR
- Imager 6.7-6.5 micron IR (WV)
- Imager Visible
- Supernational
- 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)
- Northern Hemisphere Composite
- Imager 11 micron IR
- Imager 6.7-6.5 micron IR (WV)
- Imager Visible
- Supernational
- Imager 11 micron IR
- Imager 6.7-6.5 micron IR (WV)
- Imager Visible
- West CONUS
- Imager 11 micron IR
- Imager 13 micron IR
- Imager 3.9 micron IR
- Imager 6.7-6.5 micron IR (WV)
- Imager Visible
- Sounder 11.03 micron imagery
- Sounder 14.06 micron imagery
- Sounder 3.98 micron imagery
- Sounder 4.45 micron imagery
- Sounder 6.51 micron imagery
- Sounder 7.02 micron imagery
- Sounder 7.43 micron imagery
- Sounder Visible imagery
POES-NPOESS
- Supernational
- Rain fall rate
UNIWISC
- Antarctic
- Imager 11 micron IR
- Imager 12 micron IR
- Imager 3.5-4.0 micron IR (Fog)
- Imager 6.7-6.5 micron IR (WV)
- Imager Visible
- Arctic
- Imager 11 micron IR
- 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.
%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()))
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]
<Figure size 1152x1008 with 0 Axes>
