Starting on June 19, 2024, there was a large haboob (“wall of dust”) over New Mexico and nearby regions. This was captured by both GOES-18 (1-min “mesoscale”) and GOES-16 (5-min “Contiguous U.S.”) ABI imagery. What is shown is the CIMSS true color composite imagery during the day and the “dust” RGB at night. Both animations run from approximately 21 UTC on June 19 to 04 UTC on June 20th, 2024.

Similar loop as above, but as seen from NOAA’s GOES-East.

H/T

The imagery was generated with the geo2grid software. The data was accessed via the UW/SSEC Data Services. T. Schmit works for NOAA/NESDIS/STAR and is stationed in Madison, WI.

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1-minute Mesoscale Domain Sector GOES-18 (GOES-West) Day Fire RGB, Shortwave Infrared (3.9 µm), and “Red” Visible (0.64 µm) images with overlays of the Fire Power and Fire Mask derived product (2 components of the GOES Fire Detection and Characterization Algorithm FDCA) (above) displayed signatures of the South Fork Fire and Salt Fire, which started on 17 June 2024 near Ruidoso in southern New Mexico. The GOES-18 3.9 µm Shortwave Infrared brightness temperature exhibited by the northernmost South Fork Fire first reached 137.88ºC (the saturation temperature of GOES-18 ABI Band 7 detectors) at 2012 UTC — and that fire continually exhibited the 137.88ºC saturation temperature for about 5.5 hours (ending at 0140 UTC). The 2 wildfires caused evacuations to be ordered for much of Ruidoso community.

1-minute GOES-18 True Color RGB images (source) on 17 June (below) showed the transport of dense smoke from the wildfires. Note the occasional brighter-white pyrocumulus jumps near the fire source region.

A Suomi-NPP VIIRS Day/Night Band image valid at 0809 UTC (2:09 AM local time) on 18 June (below) displayed the bright nighttime glow of the 2 wildfires near Ruidoso — with the combined smoke plume extending to the northeast and fanning out across the Texas Panhandle. Smoke occasionally restricted the surface visibility to 4-5 miles at Ruidoso Regional Airport KSRR.

1-minute GOES-18 images and derived fire products on 18 June are shown below.

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10-minute Full Disk scan GOES-18 (GOES-West) “Red” Visible (0.64 µm) images with an overlay of the Fire Mask derived product (a component of the GOES Fire Detection and Characterization Algorithm FDCA) and “Clean” Infrared Window (10.3 µm) images (above) showed 2 pulses of pyrocumulonimbus (pyroCb) clouds — exhibiting cloud-top infrared brightness temperatures in the -40s to -50s C, denoted by shades of blue to red in the 10.3 µm images — that were produced by the McDonald Fire (located just southeast of Fairbanks, Alaska) late in the day on 17 June 2024. As they moved eastward toward the Alaska/Yukon border, the first pyroCb reached a minimum cloud-top infrared brightness temperature of -53.7ºC, with the second pyroCb later reaching -54.4ºC.

A Suomi-NPP Infrared Window (11.45 µm) image valid at 2319 UTC on 17 June (below) captured the first pyroCb cloud not long after its formation — and included a cursor sample of cloud-top brightness temperatures for both the 11.45 µm (-55.58ºC) and the underlying 3.74 µm Shortwave Infrared image (+25.10ºC).  During the daytime, pyroCb cloud tops will usually exhibit significantly warmer Shortwave Infrared brightness temperatures, due to enhanced reflection of solar radiation off the smaller ice crystals found in the pyroCb anvil (reference).

A Landsat-9 Natural Color RGB image displayed using RealEarth (below) depicted the areal extent of the McDonald Fire burn scar (darker shades of brown) at 2105 UTC on 17 June, just prior to the flare-up of the wildfire that produced the 2 pyroCb clouds.

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One of the products demonstrated at the Hazardous Weather Testbed (HWT) in Norman in May and June this year was GREMLIN (GOES Radar Estimation via Machine Learning to Inform NWP) a tool created with Machine Learning (by Kyle Hilburn at CIRA) to use ABI and GLM data to create synthetic radar reflectivity fields. (This article describes the product fully). GREMLIN was trained on GOES-16 CONUS data, but in these images it is displayed over American Samoa (the product has been incorporated into the AWIPS machine at WSO Pago Pago), and it’s being compared to hourly estimates of rainfall from CMORPH-2. The images below show 6 fields (at 10-minute intervals) of simulated radar and an hourly CMORPH-2 accumulation derived independently and displayed in RealEarth.

Consider the 15 different animations shown below, that step through an event with rain over the Samoan Islands. In general, the CMORPH-2 hourly accumulations on the right agree in general with where the radar estimates derived from GREMLIN suggest rain might be falling.

What should a user do when GREMLIN does or does not show rain where CMORPH-2 is or is not showing rain accumulation. It should not make a user think “Oh, this is wrong”; rather, it means a user should investigate those regions using other satellite imagery to determine whether or not rains are occurring.

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