5-minute CONUS Sector GOES-18 (GOES-West) images (above) showed the development of a large wildfire southeast of Lytton, British Columbia (CWLY) — which produced multiple pyrocumulonimbus (pyroCb) clouds on 07 July 2026. Each pyroCb exhibited cloud-top infrared brightness temperatures in the -40s C (shades of blue) to -50s C (shades of red) — temperatures cold enough to ensure that heterogeneous glaciation had occurred at the cloud top.

As smoke from the wildfire drifted northeast, it eventually reduced the surface visibility to 3/4 mile at Kamloops CYKA (below).

This wildfire burned very hot, with the GOES-18 Fire Mask displaying Saturated Fire (yellow) pixels at 2211 UTC and 2216 UTC (below).

The coldest cloud-top infrared brightness temperature associated with these pyroCbs was -56.57 C at 0046 UTC on 08 July, after one of the clouds had drifted far northeast of the source fire (below).

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Social media lit up on 4 July 2026 with video of a strong rotating condensation funnel from Cantwell, Alaska, on the southern edge of the Alaska Range. You can see the video in this news story from KTUU-TV in Anchorage. This funnel was seen around 5 PM local time, or around 0100 UTC on the 5th. It didn’t didn’t quite reach tornado status as it never appeared to come into contact with the surface, and thus no damage was associated with this event. The NWS Weather Forecast Office in Fairbanks released the following public information statement about the event (reproduced here for accessibility and permanence:

498
NOAK41 PAFC 060027
PNSAFC
AKZ701>704-711>714-721>729-731-732-735-741>757-761>766-771>773-781-785-787-
791-795-061227-

Public Information Statement
National Weather Service Anchorage AK
427 PM AKDT Sun Jul 5 2026

NWS Anchorage received a report of a funnel cloud spotted south of the
Alpine Creek Lodge along the Denali Hwy on July 4, 2026 at about 5pm;
the report was received at about 5:49pm. The funnel was visible for
about 15 mins by onlookers, extending from the base of a towering
cumulus. Referencing videos and photos, there are no visible signs
that the funnel was in contact with the ground. Radar shows that the
parent cloud reached about 30kft and produced a single cloud-to-ground
lightning strike at 5:14pm. Based on area observations and local
topography, this funnel was likely generated by Susitna Valley winds
flowing against storm motion winds, creating enough wind shear to
generate a funnel cloud. If more information is provided that confirms
contact with the ground, this event could be reclassified.

$$

PP/AB

Geostationary satellite observations are challenging at this latitude. Cantwell is at 63.4 degrees north. The geostationary spatial resolutions are defined at the sub-satellite point over the equator. As long as you’re close to the equator the 2 km infrared resolution is pretty close. However, as the satellite scans further north, the pixels become more elongated. By the time it reaches Cantwell, the pixels are stretched to almost 6 km in the north/south direction. We can see the effects of this in the relatively coarse pixels of the visible imagery in the following loop. This is Band 2 from GOES-18, so its nominal resolution is the best we have at 0.5 km. However, at Cantwell the pixels are about 3 times the nominal resolution. Cantwell is in the center of the map, due east of the Denali National Park.

It may be challenging to interpret this loop as we’re seeing low convective clouds over a snowy mountain range, and thus we’ve got white on top of white. This is why it’s worth looking at different products to gain a fuller understanding of what’s going on. This next loop shows the Day Convection RGB, which is used to discern if convective clouds are actively growing deep. In this case, the Day Convection product is useful because it’s helping to discriminate the clouds from the snowy surface.

From this, we can see that this is becoming quite an interesting case. Normally, we’d expect deep convection to drive such a well-defined funnel cloud. But this is Alaska, and things are different up there. Prior to this event there have been only six recorded tornadoes over Alaskan land (Wikipedia has a list of tornadic events in Alaska, and the 19 April 2024 event was documented on this blog). The typical Great Plains supercell driven by the intersection of warm, moist Gulf airmasses and the edge of cooler, dry air masses generally doesn’t form in Alaska. Instead, the driving force for this particular event, as the NWS statement above describes, is atmospheric motion interacting with the local terrain. Take a closer look at the motion of the clouds, using this image as a key. The Alaskan Range arcs across the middle of the loop. There’s some lower level easterly flow that appears to be trapped by the terrain while the deeper flow is coming up from the south. This creates some significant localized wind shear that can force rotating storms even in the absence of deeper convection.

There was a VIIRS overpass a few hours before this event (around 22:30 UTC), and that true color view of that is shown here. Note how there is much greater detail in the VIIRS view, although there’s nothing in this image that makes it obvious that such an unusual event will be shortly taking place. Still, it is good for forecasters to consult the VIIRS imagery when it’s available so that one can evaluate the coarser geostationary imagery with some better context as to what’s going on.

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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.

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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.

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