Super Typhoon Bavi rapidly intensified to a Category 5 tropical cyclone (ADT | SATCON) east of Guam on 03 July 2026. Bavi was moving through a very favorable environment characterized by low values of deep-layer wind shear (above) as it was traversing progressively warmer water (Sea Surface Temperature | Ocean Heat Content). Shear, ADT, SATCON, SST and OHC products were sourced from the CIMSS Tropical Cyclones site.

10-minute Full Disk scan JMA Himawari-9 AHI Visible and Infrared images (below) provided a post-sunrise view of Bavi.

Himawari-9 Visible images (below) revealed the presence of low-altitude mesovortices within the eye of Bavi.

A daytime NOAA-21 VIIRS Day/Night Band image (below) also displayed the signature of low-altitude mesovortices within the eye.

===== 05 July Update =====

A Himawari-9 Target Sector was positioned over Super Typhoon Bavi on 05 July, providing imagery at 2.5-minute intervals. Himawari-9 Infrared Window images (above) showed Bavi — which had reached an intensity of 155 knots at 1200 UTC (JTWC discussion | ADT | SATCON) — as its eye approached the small island of Rota, located between Saipan to the north and Guam to the south. Bavi remained in a very favorable environment for intensification, with factors such as low deep-layer wind shear and warm water (SST | OHC).

The coldest cloud-top infrared brightness temperatures within the eyewall region of Bavi were around -80 C (violet pixels) — which roughly corresponded to the Most Unstable (MU) air parcel’s Equilibrium Level (EL) at a pressure of 100 hPa, according to a plot of rawinsonde data from Guam (below).

A nighttime NOAA-20 VIIRS Day/Night Band image (below) displayed the large eye of Bavi as it was southeast of the island of Rota. Peak wind gusts just prior to the image time included 67 knots at Andersen Ar Force Base, 63 knots at Guam International Airport and 55 knots at Saipan International Airport. “Side-lighting” from the Moon — which was in its Waning Gibbous phase, at 72% of full — was brightly illuminating the western eyewall’s interior vertical edge.

In a side-by-side comparison of 2.5-minute Himawari-9 Visible and Infrared Window images (below), low-altitude mesovortices were very apparent within the eye as its center passed just north of Rota.

A daytime NOAA-21 VIIRS Day/Night Band image (below) showed Bavi after its center had moved northwest of Rota. Peak wind gusts shortly before the time of the image included 96 knots at Guam International Airport, 80 knots at Andersen Air Force Base and 67 knots at Saipan International Airport. The overall peak wind gusts at those 3 METAR sites during the passage of Bavi were 96 knots at Guam, 96 knots at Saipan and 83 knots at Andersen.

The remarkably strong winds of Bavi induced large waves on the ocean surface — in fact, at 1246 UTC on 06 July the Jason-3 satellite sensed Significant Wave Height values as high as 58.70 feet west of the Mariana Islands (below).

===== 09 July Update =====

An analysis of Sea Surface Temperature from late in the day on 09 July (below) revealed a swath of cold water upwelling (brought about by the aforementioned large waves) along a portion of the track of Bavi — most notably in the area of of the Mariana Islands just north of Guam, where SST values had cooled from around 30 C on 05 July to around 27 C on 09 July.

View only this post Read Less

Meteorological satellites don’t just observe the current weather. They can also keep track of the weather’s aftermath. A great example of this is with flooding. Floods are significant economic disasters, causing upwards of $180 billion in damage (almost 1% of the US GDP) annually to structures, agriculture, and transportation infrastructure. Unlike many weather events that are over in a matter of hours to days, a flood can linger for weeks or even months.

It’s critical for emergency managers, governmental leaders, and others to be able to monitor the extent and magnitude of floods in order to plan for responses like sandbagging operations or evacuations. Of course, this is a task that can be managed by satellite. Detecting floods by satellite is conceptually quite simple: different satellite bands can be used to detect surface water. We know what pixels are supposed to be covered by surface water when water levels are normal. If we start to see other pixels start to show water coverage, then we know we have a flood.

Let’s take a look at a currently ongoing flooding event on the Illinois River in, well, Illinois. The Illinois River is one of the United States’s most important inland waterways. With over 12 billion ton-miles of shipping annually, it ranks as the third most-heavily used river behind only the Mississippi and Ohio Rivers. It’s a vital link in the global agricultural supply chain as it provides an easy way for midwest farmers to export their corn and soybeans to either the Great Lakes or down the Mississippi to the Gulf.

A map of the Illinois’s drainage basin shows that it envelops much of central Illinois with some tributaries extending into southeast Wisconsin and northwest Illinois. Frequent storms over the past month have caused significantly higher than normal rainfall totals. For the city of Peoria, right on the banks of the Illinois in the middle of the state, the observed rainfall was 134% higher than normal for the month of June, with over 5 inches extra rain than is typically seen in that month. Here’s a time series of the cumulative precipitation over the last month (4 June – 3 July 2026) at Peoria. The brown line represents the normal accumulation over that time while the green line shows the observations. It’s clear from this plot that over the last month, Peoria has experienced more than twice its typical amount of rain. Fortunately, there’s been very little additional rain over the last week.

All of that water has to go somewhere, and we can see it in the river gauges operated by the US Geological Survey. Here’s a plot of the last seven days of river levels at Beardstown, Illinois. The thick blue line shows the observed river stage, which peaked on 27th and 28th of June 2026 before starting a slow decline. Still, as late as July 3 2026, the river level is still several feet above flood stage and at least ten feet above normal.

For some time the VIIRS Flood Mapping package has been available from the Community Satellite Processing Package (CSPP) group at CIMSS, allowing detection of flooding using data from low-earth orbiting (LEO) satellite instruments. The software is based on a NOAA flood detection algorithm that was developed by Sanmei Li. Polar orbiting satellites, of course, have fine-scaled spatial resolution allowing for the detection of relatively small flooding events. However, the coarse temporal resolution of LEO satellites can prove to be a problem, especially when clouds are present. If a cloud is in the way the one or two times a day that VIIRS passes over a point, then the infrared bands used for flood detection are blocked by the cloud and it’s impossible to see the state of the surface.

Recently the ABI Flood Mapping package was released by the CSPP Geo group, allowing detection of flooding using data from geostationary satellite instruments. That package is also based on the NOAA algorithm developed by Sanmei Li. This enables flood detection at a far better temporal resolution than is possible with their existing LEO software by taking advantage of the rapidly refreshing views made possible by a geostationary platform. CSPP has users around the globe, and they can run the flood detection on arriving files and generate a composite view every hour. The higher cadence of geostationary data (every 10 minutes in the normal instrument mode) increases the probability that at least one scan will have a clear sky view, thus increasing the overall robustness of the product. Both the LEO and GEO algorithms rely on both infrared and visibly imagery, and thus are processed during daytime hours only.

Let’s take a look at how the CSPP implementation of geostationary flood detection is capturing this ongoing event. This animation shows five hours of CSPP flood detection, from 1200-1600 UTC on 3 July 2026. Blue represents pixels that are typically covered by water. The yellow, orange, and red pixels, however, represent an increasing fraction of normally dry pixels being covered by water. The gray pixels are where clouds obscured the surface preventing its characteristics from being measured. While this was a clear morning over the Illinois, the Mississippi (along the western edge of the animation) experienced more cloud coverage and thus surface water was harder to detect. This illustrates a clear advantage of the hourly product, in that you’re more likely to have clear skies at some point and can thus get more frequent updates than you would with a LEO satellite.

Let’s take a moment to compare the CSPP GEO output to the VIIRS-based CSPP LEO output. Here’s an animation of that latter product over the last several days. Due to the infrequency of the VIIRS overpasses, the flooding product is calculated on a daily cadence. It’s clear that the LEO product has an advantage in spatial resolution: the pixels are much smaller and thus more detail is readily identifiable. However, clouds cover parts of the river on multiple days because they happened to be in the way just at the time that the NOAA LEO satellites were passing overhead.

By contrast, the daily CSPP GEO flooding analysis has many more frames to select from and can therefore produce a composite view of a particular day that is far less likely to be impacted by cloud cover. Here’s the same period as the LEO product above, but from the GEO view. Note there’s much less interference from clouds. Some times persistent stratus may cover a location for an entire 24 hour period and flood values aren’t available. However, many more of the gaps from the LEO product get filled in with the GEO one.

The VIIRS and ABI flooding products are complementary. The polar orbiting detection from VIIRS not only has the higher spatial resolution when compared to ABI, it is also far more useful in Alaska and other northerly regions where geostationary resolution is degraded and polar orbiting overpasses are much more frequent; in fact, the VIIRS flooding product is available globally. However, the geostationary product has far more frequent updates in the midlatitudes. End users can benefit from consulting both products when identifying and monitoring flooding situations. Future CSPP releases are expected to include a joint VIIRS/ABI product.

The VIIRS Flood Mapping package is available to download from the CSPP LEO website, and the ABI Flood Mapping package is available to download from the CSPP Geo website. In addition, flooding products from CSPP, both LEO and GEO, can be plotted using the SSEC RealEarth online visualization platform. For GEO, look to the “Products and Layers” menu on the left-hand side. Scroll to “Flood Detection – GEO”, then select “River Flood ABI-Daily” or “River Flood ABI-Hourly” depending on your needs. For LEO, in the same “Products and Layers” menu, scroll just a bit further to “Flood Detection – Global” and select “River Flood 1 Day VIIRS Composite.”

Thanks to Graeme Martin, David Hoese, and Kathy Strabala for their insights in the operation and use of the flood detection packagers.

View only this post Read Less

1-minute Mesoscale Domain Sector GOES-18 (GOES-West) GeoColor RGB images (above) showed the dense smoke plume associated with the Aspen Acres Fire in southeastern Colorado on 29 June 2026. As the smoke plume drifted northeast across the Pueblo Airport, it restricted the surface visibility to 2 miles at times.

1-minute GOES-18 GeoColor RGB images with plots of surface observations (below) depicted wind gusts of 49-51 mph near the source of the wildfire early in the day. Mandatory evacuation orders were issued for several communities in the vicinity of this fast-moving fire.

1-minute GOES-18 GeoColor RGB images with an overlay of Next Generation Fire System (NGFS) Fire Detection polygons (below) displayed the wildfire’s large and very hot thermal signature.

The thermal signature of the Aspen Acres Fire was also very apparent in GOES-18 Day Land Cloud Fire RGB images (below).

View only this post Read Less

5-minute CONUS Sector GOES-19 (GOES-East) Infrared Window images (above) included plots of SPC Storm Reports — which showed severe thunderstorms that grew into a Mesoscale Convective System which produced wind gusts as high as 131 mph, hail as large as 2.75 inches in diameter and a few tornadoes from northwestern Nebraska to northeastern South Dakota during the overnight hours leading up to sunrise on 29 June 2026. More details about this event are available from NWS Aberdeen.

The coldest cloud-top infrared brightness temperatures were -80 C (violet pixels) — which represented a significant ~3 km overshoot of the Most Unstable (MU) air parcel’s Equilibrium Level (EL), according to a plot of rawinsonde data from Aberdeen, South Dakota (below).

View only this post Read Less