The past few days have brought devastating rain and flooding to the Australian state of Queensland, in the country’s northeast. The community of Paluma received more than 1.4 meters (4.5 feet) of rain over the weekend, and damage to roads and bridges carried out by the torrents of water have effectively cut off parts of northern Queensland from the rest of the country. Thousands of residents have been told to evacuate While the rains have started to abate, the damage will be felt for many months.

The Blended Total Precipitable Water (TPW) product provides a quantitative assessment of just how much water was present in the atmosphere. TPW is measured with microwave instruments, and thus can be measured even in the presence of clouds. However, microwave sounders are only found on polar-orbiting satellites which means that a particular location is going to be observed by several different satellites over the course of a day. Satellites from NOAA, NASA, the US Department of Defense, EUMATSAT, and others all are making TPW observations operationally. The Blended TWP product corrects across all of these instruments with their unique traits to create a globally continuous product that updates at a temporal resolution that is far finer than can be addressed by a single satellite.

 This Blended TPW loop shows the evolution of the TPW from 0300 UTC to 1700 UTC on 31 January 2025. It is clear that northeastern Australia is beset with moisture-laden air, as TPW values are easily over 70 mm.  

The MIMIC-TPW2 hourly composite product is created from retrievals using AMSU-B and MSU onboard NOAA-18, NOAA-19, Metop-A and Metop-B; it also uses retrievals from ATMS onboard the Suomi-NPP, NOAA-20 and NOAA-21 polar orbiting satellites. In a 3-day animation of MIMIC-TPW2 (below), a cyclonic gyre of high TPW (with values often exceeding 70 mm, denoted by lighter shades of violet) is evident in the vicinity of far northern Queensland.

The sounding from Willis Island, about 280 miles east of the Queensland shore in the Coral Sea, affirms how moist the air was. In effect, the atmosphere was saturated throughout the whole depth of the troposphere.

The ensuing convection was extremely vigorous, with cloud top temperatures dropping below -80 C as can be seen in the zoomed-in loop of the 10.4 micron window channel from the geostationary Himawari-9 satellite. Numerous overshooting tops are present, and given the high altitude of the tropical tropopause, those clouds are topping out at over 46,000 feet (14 km). With very little lateral propagation of these storms, it’s easy to see why the rainfalls were so substantial.

Image sources above include: CIRA RAMMB Slider, University of Wyoming Sounding Archive, CIMSS Tropical Cyclones

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A longer animation of Himawari-9 Infrared images (below) revealed that cloud-top 10.4 µm infrared brightness temperatures were occasionally -90ºC or colder (yellow-to-gray pixels embedded within darker purple regions) — for example, near Cairns YBCS at 1030 UTC.

A Himawari-9 Rain Rate derived product from the RealEarth site is shown below — Extreme rates (shades of red to violet) were associated with some of the convective clusters.

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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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