Vermont was hit with its worst flooding in over a century, making it one of the worst natural disasters in the state’s history. Rains dumped up to nine inches from Monday, July 9 and into Tuesday, July 10, 2023. Major roads and highways were shut down, and more than 200 rescues were carried out.

Sentinel 2A, a polar-orbiting satellite operated by the European Space Agency, captured imagery of this flooding. CIMSS scientist Sam Batzli made this data accessible on RealEarth, seen in the image below.

CIMSS scientist Danielle Losos created a Normalized Burn Ratio over the Sentinel 2A imagery and produced some interesting results. It turns out that burn scars and water both reflect differently than the surrounding landscape, so flooding shows up in a Normalized Burn Ratio. Two examples can be seen below.

You can explore the Normalized Burn Ratio over parts of Vermont for these dates using RealEarth. As of Thursday, July 13, Vermont may be due for more rain, and a new flood watch may be put into place by Thursday afternoon.

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GOES-16 (GOES-East) Day Land Cloud Fire RGB, Shortwave Infrared (3.9 µm), “Red” Visible (0.64 µm) with an overlay of the Fire Temperature derived product and Visible images with an overlay of the Fire Power derived product — both derived products being components of the GOES Fire Detection and Characterization Algorithm FDCA — (above) showed thermal signatures associated with the Pallet Fire near Coloma in central Wisconsin on 10 July 2023. Wind gusts at the nearby Wautoma Airport reached 23 knots — and the smoke plume briefly reduced surface visibility at that site to 5 miles at 23 UTC.

Cursor-sampling at 2126 UTC (below) indicated that the warmest 3.9 µm brightness temperature was 108.82ºC, with a peak Fire Power value of 1524.24 MW.

GOES-16 True Color RGB images from the CSPP GeoSphere site (below) highlighted the distinct smoke plume created by this wildfire.

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GOES-16 Visible (Band 2, 0.64 µm) and Infrared (Band 13, 10.3 µm) imagery at 2316 UTC on 9 July 2023, above, show cloud-top features commonly associated with intense convection off the east coast of Florida, to the southeast of Cape Canaveral. RADARSAT-2 overflew this region at the same time, and derived SAR wind estimates, toggled above with the ABI data, exceeded 50 knots (the yellow/red enhancement) with the southern convective cell.

Do you think those SAR wind observations are credible? Sometimes when SAR winds are very strong in regions of convection, the enhanced wind signal occurs because of reflection off ice crystals within the cloud. When that happens, the Normalized Radar Cross Section (NRCS) field will frequently acquire a feathery look. That is not occurring on this day, as shown in the toggle below of derived wind speed and NRCS, at least not in the region of the strong convection just east of the Florida coast. It’s likely that winds over the ocean here were close to 50 knots in strength.

NOAA/CIMSS ProbSevere (version 3) identified the convective cell at 2316 UTC, as shown below. However, probabilities were quite small — less than 10%, albeit with a maximum shortly after 2300 UTC (Click here to see a ProbSevere readout for the Radar Object associated with the southern part of the convection, the one that is oriented north-south). A Special Marine Warning was nevertheless in effect in the region of strong winds. Probabilistic guidance such as ProbSevere should be used in concert with other products to arrive at a warning decision. In this case the other products argued persuasively for warning issuance.

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Widespread clouds can easily hide the location of active precipitation. When that happens, well-timed microwave information can show where precipitation is most likely. In the slider example above, Aqua MODIS infrared imagery with a grey-scaled enhancement (collected over Guam just before 0350 UTC on 9 July) shows regions of convection west and south of Guam (circled in yellow). Where are the heaviest rains located, and where are the stratiform rains, and what regions are mostly dry, but under clouds? GCOM-W1 AMSR-2 data (collected over Guam around 0403 UTC on 9 July) can give information to address those questions. In this case, it reveals the regions underneath the clouds where strongest convection is most likely. This example highlights why JPSS data can be so vital in regions where radar coverage is lacking (or when the radar goes down!): because the microwave data can give important information about what’s going on underneath the cloud canopy.

Of course, enhancements to the infrared imagery can also provide information, especially about how things are evolving with time. That’s shown in Himawari-9 infrared imagery (Clean window, Band 13 — 10.4 µm — and Upper-level Water Vapor, Band 8 — 6.25 µm) shown below. Note the appearance and decay of multiple cold cloud tops in the infrared data as convective towers evolve with time. A best practice is to combine the information gained by the snapshot given by the microwave data and use that information to infer what is occurring underneath the clouds in the geostationary data.

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The imagery below compares NOAA-20 VIIRS imagery and rain rate derived from ATMS (at 0322 UTC) to the same GCOM AMSR-2 imagery as shown above.

Many thanks to Brandon Aydlett, Science and Operations Officer, WFO Guam, for the Aqua MODIS, Suomi-NPP and GCOM-W1 AMSR-2 imagery that was downloaded at the WFO GUM L/X Band direct broadcast antenna and processed with CSPP software.

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