Antonio B. Won Pat International Airport on Guam experienced a record wet 7 January this year when 3.32″ of rain fell, mostly between 1700 UTC/6 January and 0500 UTC/7 January. Himawari-9 Band 13 imagery, above, for the 3 days ending at 0000 UTC 08 January 2025, show a region of thunderstorms approaching the Marianas. A slower animation covering the times of the heavy rainfall is below; the heavy rain was fairly isolated; much of the Marianas remained dry.

A closer look using 10-minute Himawari-9 Infrared images centered on Guam International Airport PGUM (below) showed the series of convective storms that produced the bulk of the record-setting rainfall (plot of surface observations). Cloud-top infrared brightness temperatures briefly reached -80ºC (violet pixels) with one of the convective elements.

It is noteworthy that the Total Precipitable Water value of 63.33 mm (2.49 in) derived from 7 January / 0000 UTC PGUM rawinsonde data exceeded the previous record TPW value of 2.34 in for that date/time (based on SPC climatology).

10-minute Himawari-9 Visible and Infrared images (below) displayed the rapid development of a convective cell which produced a brief period of heavy rain at PGUM as it traversed the island.

The large-scale conditions that allowed the heavy rain were well-forecast. GFS forecasts of the Galvez-Davison Index (GDI) (source) from the forecast started at 1800 UTC on 4 January 2025 are shown below, and they show a narrow tongue of higher values moving over the Marianas that corresponded with the time of the heavy rains.

MIMIC Total Precipitable Water (TPW) fields, below, from 0000 UTC 6 January to 0000 UTC on 8 January 2025, show abundant moisture moving over Guam (at 144^oE Longitude, 13.4^oN Latitude)

The Guam forecast office of the National Weather Service (WFO GUM) receives polar orbiting data from a Direct Broadcast antenna onsite (signals are processed by CSPP software). True-color VIIRS imagery from ca. 0300 UTC on 7 January is shown below, and a line of convection bisecting Guam is apparent.

CSPP also processes microwave imagery, and the computed MIRS estimates of TPW are shown below. The MIRS diagnostics also show the enhanced amount of TPW over the southern Marianas as the rain fell. TPW values dropped quickly by mid-day (UTC) on the 7th (at the end of the animation below).

My thanks to Landon Aydlett, WCM on Guam for alerting me to this wet event, and to Douglas Schumacher, CIMSS, for the Direct Broadcast imagery.

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1-minute Mesoscale Domain Sector GOES-18 (GOES-West) Shortwave Infrared (3.9 µm) along with Red Visible (0.64 µm) + Fire Mask derived product images (above) displayed a pronounced thermal signature associated with the Palisades Fire located between Malibu and Santa Monica in Los Angeles County, California on 7th January 2025. The 3.9 µm infrared brightness temperatures at the fire origin location began to increase at 1824 UTC, then rapidly increased in intensity and areal coverage for the next several hours as the wildfire exhibited extreme behavior (due to dry fuels and strong Santa Ana winds). The increase in 3.9 µm infrared brightness temperatures at the 1824 UTC fire start time can be seen within the red box below (the NGFS-identified fire start time was 1825 UTC).

1-minute GOES-18 True Color RGB images from the CSPP GeoSphere site (below) showed the offshore transport of smoke from the Palisades Fire — and a few pyrocumulus jumps in the vicinity the wildfire were seen later in the day.

===== 8th January Update =====

During the subsequent nighttime hours, 1-minute GOES-18 Shortwave Infrared and Fire Mask images (above) showed the Palisades Fire thermal signature as it expanded westward toward Malibu. At 0500 UTC the peak wind gust at Burbank (KBUR) was 73 kts or 84 mph — and at 0545 UTC the peak wind gusts were 58 kts or 67 mph at Van Nuys (KVNY) and 51 kts or 59 mph at Santa Monica (KSMO). According to CAL FIRE updates, during the 12-hour period from 0000-1200 UTC on 8th January the fire more than doubled in size (growing from 1262 acres to 2921 acres).

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GOES-19 is slated to become the next operational GOES-East in April. Despite being trained on GOES-16 data, CIMSS and NOAA scientists have been busy evaluating the AI lightning-prediction model, LightningCast, on GOES-19 output to prepare for the operational transition and see how adaptable the model is.

Overall, the LightningCast output on GOES-19 looks to be in-line with performance on GOES-16. There are still some data-quality issues that the ABI imagery team is working on to fix, and once resolved, we can quantitatively compare LightningCast output from the two satellites more accurately.

In Europe, the Meteosat Third Generation (MTG) satellite, Meteosat-12, recently became operational. We have been able to get LightningCast to run on data from Meteosat-12’s Flexible Combined Imager (FCI), with surprisingly good results. We say “surprisingly,” because we expected that the differences between ABI’s and FCI’s spectral response functions and differences in channel resolutions would generate poor lightning predictions, since the model was trained on data only from GOES. On the contrary, the predictions look reasonable considering they are applied to FCI data with no re-training or fine-tuning. The animation below shows the sequence of LightningCast predictions, FCI day-cloud-phase-distinction RGB (background), and MTG’s Lightning Imager (LI) flash-extent density (foreground), over a scene in south eastern Africa, over the countries of Mozambique and Zimbabwe.

There are still some data quality issues with FCI (e.g., co-registration errors) and LI, but the predictions look reasonable for a first application to the new sensor. In this scene, there is over-prediction in the maritime region and under-prediction for some regions with lightning initiation. But we believe that with fine-tuning methods on a limited amount of FCI and LI data, LightningCast will perform well with MTG data without a significant re-training.

For users who want to run LightningCast on these sensors, the CSPP-Geo LightningCast package will soon have a patch that will enable it work fully with GOES-19, and an update is planned for full support of MTG data, though the date is not known at this time.

H/T: LightningCast software has made substantial use of the Satpy library to read and visualize data from geostationary satellites such GOES-R, MTG, and Himawari.

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GOES-16 Day Cloud Phase Distinction RGB imagery at 1801 UTC on 6 January 2025, above, show evidence (in greenish yellow) of a glaciated band of cloudiness that is a Lake Effect snow band that deposited from 3-6″ over extreme southeast Wisconsin, in Kenosha and Racine counties. The toggle with the radar emphasizes how Day Cloud Phase Distinction can be used (in daytime) to highlight precipitating lake-effect snow bands. Visible imagery at the same time is shown below. The character of the precipitating clouds is different, and that might help you identify the precipitating clouds from the non-precipitating ones.

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The use of Day Cloud Phase Distinction to highlight precipitating clouds has been discussed before on this blog, here and here and here, for example.

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