10-minute Full Disk scan JMA Himawari-9 True Color RGB and Ash RGB images created using Geo2Grid (above) showed the volcanic cloud resulting from an eruption of Dukono (on the Indonesian island of Halmahera), which began at 2241 UTC on 07 May 2026. Changes in wind direction/speed with height resulted in a complex transport pattern of the volcanic cloud material.

Radiometrically retrieved products from the NOAA/CIMSS Volcanic Cloud Monitoring site (below) were useful to further characterize the Dukono eruption cloud.

Maximum ash heights (above) were in the 10-14 km range, while ash loading values (below) were quite high in some parts of the volcanic cloud.

Ash effective radius values (below) were also quite high in some parts of the volcanic cloud.

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Overlapping portions of 1-minute Mesoscale Domain Sectors provided GOES-19 (GOES-East) imagery at 30-second intervals — and Infrared images with plots of SPC Storm Reports (above) showed thunderstorms that produced tornadoes, large hail and damaging winds across parts of southern and central Mississippi and Alabama from the late afternoon into the early evening hours on 06 May 2026. Pronounced enhanced-V storm-top signatures were exhibited by several of the severe thunderstorms — and the coldest cloud-top infrared brightness temperature associated with pulses of overshooting tops was -80ºC (violet pixels embedded within brighter white areas). Multiple long-track tornadoes (one of which was EF3-rated) moved across southern Mississippi, and were responsible for widespread damage and several injuries.

1-minute GOES-19 Infrared images (below) included plots of NWS Warning polygons — which revealed a few Tornado Emergency (bold red) polygons in southwestern Mississippi (0000 UTC | 0010 UTC | 0056 UTC | 0105 UTC) along the path of the EF3 tornado.

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During a period when American Samoa had been under a Flash Flood Watch, the Weather Service Office at Pago Pago requested that a GOES-18 (GOES-West) Mesoscale Domain Sector be positioned over the region (due to their lack of radar, satellite imagery can be a critical tool for monitoring the development of deep convection). 1-minute GOES-18 Infrared imagery with overlays of GLM Flash Points and the Total Precipitable Water (TPW) derived product (above) showed clusters of deep convection with intermittent lightning activity that moved across the main island of Tutuila (located just south of where the imagery is centered) as well as the smaller Manu’a Islands (~65 miles to the east) during a 15-hour period on 04-05 May 2026. At the Pago Pago METAR site on Tutuila, most of their calendar day 24-hour precipitation for 04 May (2.21 inches) occurred during the period shown in the GOES-18 animation above — and a Flash Flood Warning was issued at 0154 UTC on 05 May.

The coldest cloud-top infrared brightness temperature exhibited by storms in the vicinity of Tutuila was -78.14ºC (above) — which was at an altitude near the Most Unstable (MU) air parcel’s Equilibrium Level (EL), according to rawinsonde data from Pago Pago (below). That sounding also depicted an atmosphere which was very moist and unstable, with parameters that were favorable for the further development of deep convection. In addition, rain shower and thunderstorm activity was enhanced by the presence of a surface trough / stationary front in the vicinity of the Samoan Islands (05 May surface analyses: 0000 UTC | 0600 UTC | 1230 UTC).

It is noteworthy that according to the climatology of TPW for all 0000 UTC soundings at Pago Pago (below), the TPW value of 2.81 inches sampled at 0000 UTC on 05 May 2026 was significantly higher than the previous maximum value on record for that particular time/date (2.63 inches).

The MIMIC Total Precipitable Water product (below) showed the band of high moisture that was moving southeast across the Samoan Islands during this flash flooding event.

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Version 1 of the NOAA/CIMSS LightningCast model uses GOES-R ABI images to predict the probability of lightning in the next 60 minutes at any given location. It is being transitioned to NOAA/NESDIS operations. LightningCast v2 has been developed and is being evaluated at NOAA’s Hazardous Weather Testbed. Version 2 incorporates MRMS Reflectivity at -10^oC, which is a well-known product used to help determine the ice content in convection. We’ve found that version 2 improves short-term lightning predictions across the contiguous U.S. (CONUS), with a very small reduction in performance in regions outside CONUS. Here are some examples.

Dallas / Forth Worth Metro

On April 29, the Dallas / Fort Worth metro region was socked in with dense mid- and high-level cloud cover. There was very little contrast or texture in the cloud tops in the ABI imagery. Thus, LightningCast v1 had low probabilities until convective cloud features began to poke up from the thick cloud canopy and the cloud-top brightness temperature began to cool. However, LightningCast v2 had moderate-to-high probabilities much sooner, owing to the Reflectivity -10^oC predictor.

In two convective areas southwest of Forth Worth and over Fort Worth proper, LightningCast v2 provided 17 minutes and 5 minutes of additional lead time to the first flashes detected by GLM, respectively, compared to version 1. When plotted as a line graph over TCU’s Amon G. Carter Stadium, the version 2 probability shows a clear uptick 5 minutes before version 1. Later, version 2 remains higher than version 1 during another burst of lightning.

Pennsylvania

Meanwhile, Pennsylvania was also socked in with dense cloud cover on the same day. Some shallow convection remained hidden to version 1, whereas version 2 had higher probabilities over the electrified region. While this was a difficult case due to the marginal nature of the convection, version 2 still provided more reliable guidance.

Mississippi Valley

In the central Mississippi Valley, LightningCast version 2 correctly had higher probabilities in southern Illinois, western Kentucky, eastern Kansas, and eastern Arkansas compared to version 1. It correctly had lower probabilities in western Indiana, as well. All are areas of adequate radar coverage.

We’ve demonstrated that the radar predictor adds a lot of value over CONUS, while the model has in general learned to rely on satellite inputs where radar coverage is absent. Version 2 isn’t better in every instance, but overall, it provides superior guidance.

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