Joint Polar Satellite System (JPSS) data are now available online as part of the NOAA Open Data Dissemination (NODD) Program; at present, the data are available via Amazon Web Services (with future capabilities planned for Azure and Google). Global Suomi-NPP, NOAA-20 and NOAA-21 data (including Sensor Data Records (SDRs) and Environmental Data Records (EDRs) from ATMS, VIIRS and OMPS — and also geolocation files) are all available for free download. How can you create a display of the imagery, as shown above? The CSPP Software package Polar2Grid (v 3.0, available for download here) produced the image you see above.

After downloading Polar2Grid, you’ll need to determine the times of the data you wish to display; the data on the Cloud are saved off in roughly 90-second intervals. I consulted the SSEC Polar Orbit page (link) to find a (random) orbit from NOAA-21, where the orbit chosen is an ascending pass over Indonesia between 0320 and 0330 UTC on 5 May. The next step is to go to the Amazon Web Service data repository and find the I01 SDRs for the imagery to be created, and the geolocation information. Those webpages are shown below, with the files with data from 0320 to 0330 highlighted in the toggle. Note that the timestamps of the eight I01 and geolocation files are the same: 03:20:13.5, 03:21:37.7, 03:23:03.6, . . . 03:30:11.3. Download these files to a directory on the machine on which Polar2Grid is also installed. Note that these ‘GIMGO’ geolocation files are not terrain-corrected; the AWS site does include a directory (VIIRS-IMG-GEO-TC) that includes terrain-corrected geolocation files (‘GITCO’) that would be appropriate to use in regions of high terrain.

The Polar2Grid calls (remember that the environment variable $POLAR2GRID_HOME must be set) to create the imagery are straightforward:

$POLAR2GRID_HOME/bin/polar2grid.sh -r viirs_sdr -w geotiff -p i01 -f ./Cloud/*I01*.h5

(the ‘GIMGO’ files downloaded are also in that ./Cloud/ directory) reads the viirs_sdr I01 files and creates a geotiff image, and

$POLAR2GRID_HOME/bin/add_coastlines.sh --add-coastlines --add-grid --grid-D 10.0 10.0 --grid-d 10.0 10.0 --grid-text-size 20 noaa21_viirs_i01_20230505_032013_wgs84_fit.tif

adds coastlines and a latitude/longitude grid to the geotiff file created, and creates a png file (shown here at full resolution; VIIRS Image Files at native resolution are very large — this 10-minute one is 6500×7600 pixels! — because they’re at 375-m resolution; note also that the flag --coastlines-resolution f was added to the ./add_coastlines.sh call for the full-resolution image). The reduced-size image above was reduced in size and annotated using ImageMagick.

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Sentinel-1A overflew the Hawai’ian Islands near sunset on 4 May, as shown above (Sentinel-1A SAR Imagery is available online here and here). The toggle above compares the derived winds with GOES-18 Visible imagery (Band 2, 0.64 µm); the visible data enhancement has been changed (that is, brightened) from the default range of 0 to 130 to just 0 to 5 for this post-sunset scene. Winds approaching 30 knots (orange/red in the enhancement used) are common in the bands of wind between the islands. The regions of relatively calm winds (purple and blue in the enhancement used) in between the strong wind bands are where cloud bands exist, as shown both in the toggle above and the side-by-side image below.

The presence of the cloud bands suggests that surface convergence is occurring in between the bands of strong winds.

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10-minute Full Disk sector GOES-18 (GOES-West) Fire Temperature RGB, “Clean” Infrared Window (10.3 µm), Cloud Top Height and Cloud Top Temperature (above) showed the formation of a pyrocumulonimbus (pyroCb) cloud produced by a wildfire southeast of Edson, Alberta (station identifier CYET) on 04 May 2023. Cloud-top 10.3 µm infrared brightness temperatures were as cold as -61ºC, while the multispectral Cloud Top Temperature derived product revealed values as cold as -66ºC. Cloud Top Height values reached 39,000 feet. It should be noted that these Full Disk sector images and products are (unfortunately) provided at a reduced spatial resolution in AWIPS.

A plot of 0000 UTC rawinsonde data from Edmonton, Alberta (below) indicated that the pyroCb cloud-top temperatures colder than -60ºC were close to the tropopause temperature, suggesting that some smoke and cloud material may have been injected into the lower stratosphere.

GOES-18 True Color RGB images from the CSPP GeoSphere site are shown below.

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A long-wave diffluent trough centered over California and Nevada was helping to force benign convection along the Laramie Mountains, which run roughly from Laramie to Casper, Wyoming.

The ProbSevere LightningCast model, which uses AI and GOES-R data to predict the probability of lightning in the next hour, was able to highlight this convection before the first flashes occurred. Lead time to the initial flashes ranged from 10 to 30 minutes, measured from the 25% probability of lightning contour.

One interesting feature in this animation is the packing of the probability contours. The storms are moving generally from south to north. The contours are much more packed along the south and west edges of the region of convection (i.e., where convection is moving away from), while they are more diffuse along the north and east edges (i.e., where the convection is moving towards. This indicates that the model is (at least in part) accounting for the motion of the storms in the next hour. LightningCast expects the storms to move north, which is what they are indeed doing. This was a very surprising result, since LightningCast was trained with samples that used only one snapshot or timestamp of satellite data.

From GOES-West (GOES-18) in Figure 2, we can see the same effect, though perhaps not as pronounced. But keep in mind the satellite viewing geometry is much different in Wyoming from GOES-East versus GOES-West. And while next-hour motion of storms is not always well predicted, it is nevertheless encouraging that LightningCast is able to discern motion at times from only one snapshot of data. Forecasters often look at animations of satellite data to make nowcasts, and we believe that training the model with “videos” of data rather than images will further enhance its ability to project lightning threats in the near future. One downside is that this greatly increases the complexity and computational cost to create such a model. However, recent developments in AI/ML modeling show that training with video imagery (and predicting video imagery) is becoming more feasible.

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