5-minute PACUS Sector GOES-18 (GOES-West) True Color RGB images from the CSPP GeoSphere site (above) displayed the bright white signature of fresh snow cover on the summits of Mauna Kea and Mauna Loa on the Big Island of Hawai`i on 1st February and 2nd February 2025. This snowfall occurred as a strong storm brought severe thunderstorms and heavy rainfall to parts of the island chain on 30-31 January.

30-meter resolution Landsat-8 “Natural Color” RGB imagery from the RealEarth site (below) provided a more detailed view of the snow cover (brighter shades of cyan) on 1st February.

===== 3rd February Update =====

Toggles between Sentinel-2 True Color RGB images (source) of Mauna Kea (above) and Mauna Loa (below) showed the 2 summits before snowfall (on 29th January) and after snowfall (on 3rd February).

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The ProbSevere LightningCast version 1 (v1) model uses machine learning and GOES-R Advanced Baseline Imager (ABI) data to predict the probability of lightning in the next 60 minutes. This version will be operational at NOAA later in 2025.

As research and development continues, a new version of the model (LightningCast v2) adds Multi-Radar Multi-Sensor (MRMS) Reflectivity at -10^oC as a predictor. Reflectivity at -10^oC is well correlated with imminent lightning activity due to its ability to depict hydrometeors in the mixed-phase region of convection (generally 0^oC to -20^oC). Other radar-derived parameters are being investigated as well.

Preliminary results demonstrate that the radar-derived predictor adds value to the problem of short-term lightning prediction, without diminishing the power of the satellite predictors—that is, the model appears to have learned and uses the strengths of each data source for making better predictions.

Here are two recent examples. First, in Ohio and Pennsylvania, warm air advection at 850 and 700 mb forced some elevated but shallow thunderstorms.

A short-term model profile from the NAM 3-km model also shows the elevated but abbreviated extent of CAPE.

The first animation below is from LightningCast v1. The shallow nature of the storms and the thick layer of ice clouds above the convection obscures key signatures for the satellite-only model, resulting in poor probabilistic guidance.

The animation below uses the LightningCast v2 model. While not a cure-all, the reflectivity at -10^oC clearly helps the model provide better guidance to lightning, albeit with little lead time to lightning initiation in this case.

The day prior, weak 850 mb warm air advection forced convective development in northeast Colorado and southwest Nebraska. The thermal profile appeared to be too cold to generate much CAPE or lightning.

However, from a satellite perspective, the convection certainly looks like it could produce lightning. Thus, the LightningCast v1 output shows high probabilities of lightning (animation below).

But the reflectivity at -10^oC predictor generally has convective cores of only 25-30 dBZ. Typically 35-40 dBZ is needed for lightning at this isotherm.

The radar predictor helped reduce the false alarm predictions of lightning markedly. See the animation of LightningCast v2 predictions below.

Overall, we’ve found that LightningCast v2 improves the critical success index over the contiguous U.S. (CONUS), while not harming predictions outside of the CONUS. In the image below, red regions show improvement of v2 over v1, whereas blue regions show degradation of performance in v2, with respect to v1 (note that this is a limited sample).

An assessment of lead time to lightning initiation (LI) has shown that rather than diminishing lead time to LI, LightningCast v2 actually appears to increase lead time to LI over the CONUS by a small amount. Work is on-going to quantify how much ABI predictors alone increase lead time to LI ahead of a radar-only lightning nowcasting model. We hope to have forecasters evaluate LightningCast v2 at the 2025 Hazardous Weather Testbed.

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High spatial resolution satellites like the Landsat series operated by NASA and the US Geological Survey, or Sentinel operated by the European Space Agency, are often used for land process investigations. Their very high spatial resolution comes at the cost of less frequent overpasses over specific locations, and so they aren’t used as regularly for meteorological purposes as the coarser-but-more-frequent observations from low-earth orbiting instruments like VIIRS or the multiple observations per hour that geostationary satellites produce. However, phenomena of interest to meteorologists can still be found in these satellites if they happen to fly over at the right time.

A month’s worth of observations from the Sentinel-2 satellite shows this very well. Depending on the channel, spatial resolution from Sentinel-2 can be as fine as 10 m which results in very highly detailed imagery of Earth’s surface and the clouds above it, but together the two Sentinel-2 satellites only pass over a location once every five days. Here, several images from the month of January 2025 are shown depicting Chicago and the southern tip of Lake Michigan. Early on in the month, the lake was ice-free as the water temperatures remained well above freezing. By mid-month, however, temperatures plunged into the single digits Fahrenheit overnight and ice began to form in the shallower regions near the lakeshore (for example, see this 15 January blog post).

Consistent offshore winds helped push newly-formed ice into the middle of the lake. At times, these winds can be measured from space using the Advanced Scatterometer (ASCAT) instrument deployed aboard EUMETSAT’s Metop series of polar orbiting satellites which derives wind speed and direction from the radar reflective properties of surface water waves. The Great Lakes are large enough that useful wind measurements can be obtained from them via satellite. For example, ASCAT winds from 20 January show strong westerly flow from the land and over the lake.

As the wind pushed newly-formed ice away from the shore, new ice could form in the same shallow regions. The very low temperatures in mid-to-late January(the high temperature in Chicago was only 2 F on 21 January) caused a significant amount of western Lake Michigan to freeze.

The Sentinel overflights also uncovered some very interesting industrial impacts on cloud formation. The below image is from 24 January 2024. Very faint plumes are visible in the true-color imagery stretching to the northeast, originating from downtown Chicago and the steel mills and oil refineries of northwest Indiana. These plumes track across Lake Michigan where they serve as cloud nucleation that enhances the development of the elevated convection seen off of the western edge of the state of Michigan. In essence, the output of these significant areas of heavy industry is seeding clouds that form dozens of kilometers downwind. The very-small scale of these plumes means they are difficult to identify when using almost any other satellite system.

A zoomed-out view, showing the size and scope of these enhanced cloud regions, comes from the more coarsely-resolved Sentinel-3 Ocean and Land Color Instrument (OLCI). In this case, it appears there is additional seeding from industry in southwestern Wisconsin.

While these instruments that are focused on land studies won’t have applications for every day in operational meteorology, these examples show that they are still useful for gaining a larger-picture view of the environment and uncovering interesting processes occurring at scales that might be too small to otherwise be detected.

Imagery source: Copernicus Browser, https://browser.dataspace.copernicus.eu/

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The potent cyclone (surface analysis, an airmass RGB animation from the College of Dupage website is below) that caused severe weather over Hawaii (blog post) is also drenching the islands with considerable rains. The animation of MIMIC Total Precipitable Water, above, shows the storm drawing tropical moisture northward from the Intertropical Convergence Zone. The airmass RGB, below, also shows that connection to the tropics, with the green area in the RGB that is moving northward over Hawaii.

How have various estimates of precipitation quantified the rains that have fallen? Hourly CMORPH-2 Rain Estimates captured at this RealEarth site (enter ‘CMORPH’ into the Search box; this website has data through 31 December 2024), below, capture the progress of the heavy rain band across the islands, mostly west to east, but with a couple of seemingly backward steps.

In contrast, GOES-18 GREMLIN estimates of MRMS radar reflectivity, shown below with GOES-18 mid-level water vapor infrared imagery, shows steady eastward progress to the heavy rainband. A band of lighter precipitation persists over Kauai however. The animation below suggests the backward steps in the CMORPH hourly precipitation shown above are artifacts that bear investigation. A discussion on how GREMLIN rain estimates are used over Pago Pago in the south Pacific is available here.

There is a direct broadcast antenna at Honolulu Community College (link) and microwave data from that site can be used to derive instantaneous rain rates (via MIRS algorithms). The animation below shows three snapshots between 0715 and 1215 UTC.

How do the instantaneous rain rates compare with GREMLIN MRMS estimates? Three comparisons are shown below. The figures are qualitatively similar.

When evaluating heavy rains, there are many products available to a forecaster. Use them all to increase confidence in the diagnostics.

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