The animation of GOES-17 visible imagery (Band 2, 0.64 µm), above, (created at the CSPP Geosphere site) shows the characteristic signal of Sun Glint — the moving bright region. Sun glint (also discussed on this blog here, here, here, here and — for moonglint: here!) occurs when radiation from the Sun reflects off the ocean surface and is detected by the satellite sensor, in this case the GOES-17 ABI. On the day displayed (17 November), this reflection was pronounced because of light winds and flat seas. The flatter the sea, the more pronounced and intense the reflection; a useful analogy is flat and wrinkled aluminum foil for flat and wavy seas, respectively. You would expect to see a more concentrated reflection off the flat aluminum foil.

How light were the winds? MetopB and MetopC scatterometer winds, shown in tandem below, from 17 November (from this site), show very light winds to the northeast of the Samoan islands.

Conditions that support concentrated sun glint will also affect the computed sea-surface temperatures (SSTs). That is shown below. Note how warm the SSTs become (>93^oF at 0100 UTC)!

Should you believe the very warm SST values? If the seas are flat and winds are near calm, the surface skin of the ocean can become very warm, because mixing in the upper inches of the ocean is minimal absent forcing by winds and waves. The SST algorithm incorporates information from the shortwave IR band (Band 7 on ABI at 3.9 µm) and does account for sun glint (see pp 27-28 in the Algorithm Theoretical Basis Document). The image below shows 0100 UTC Visible (0.64 µm) on the left, Shortwave IR (3.9 µm) in the middle and computed SSTs on the right.

In winds are very light, expect to see warmer SSTs than in surrounding waters where winds are stronger (and vertical mixing in the ocean is therefore greater). However, pay attention if an increase in SSTs accompanies sun glint. The difference of 5^oF shown above is likely contamination from reflected solar insolation.

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As an anomalously-strong upper-tropospheric jet streak was moving across the Mid-Atlantic region on 20 November 2022, GOES-16 (GOES-East) Upper-level Water Vapor (6.2 µm) images (above) displayed numerous 6.2 µm Derived Motion Wind (DMW) vectors having speeds of 190 knots and higher — with the jet streak axis passing just to the north of the Wallops, Virginia rawinsonde site (KWAL). Derived Motion Wind speeds as high as 201 knots and 202 knots were seen shortly before the launch of the 12 UTC Wallops sounding.

Plots of Wallops, Virginia rawinsonde data at 00 UTC and 12 UTC on 20 November (below) showed that the maximum wind speeds were at the 213 hPa level (187.8 knots) at 00 UTC and the 206 hPa level (189.8 knots) at 12 UTC.

A plot of 250 hPa wind speed climatology for the Wallops rawinsonde site (from SPC) includes the 12 UTC value on 20 November 2022 (shown by the black circle) — which indicated that the 183-knot value measured during that 12 UTC sounding ascent was the fastest 250 hPa wind speed for the month of November at that site.

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1-minute Mesoscale Domain Sector GOES-16 “Clean” Infrared Window (10.3 µm) images (above) include an overlay of GLM Flash Extent Density — which showed rapidly-cooling cloud-top infrared brightness temperatures (and intermittent lightning) over the eastern half of  Lake Erie, associated with the initial impulse of lake effect snow (LES) immediately downwind of the lake after sunset on 17 November 2022. Snowfall rates quickly ramped up to 3 inches per hour at Buffalo (station identifier KBUF), with lightning reported. Along the north shore of Lake Erie, about 20 miles west of Buffalo, a peak wind gust of 60 knots was recorded at Port Colborne, Ontario (station identifier CWPC) at 0211 UTC on 18 November.

In the wake of this initial impulse, a dominant LES band became established along the axis of Lake Erie, which persistent during the entire night and into the following morning — snowfall accumulations were as high as 36.0 inches by 1530 UTC (10:30 AM EST).

After sunrise on 18 November, 1-minute GOES-16 Day Cloud Phase Distinction RGB images (below) indicated that much of the dominant LES band had cloud tops of mixed phase (supercooled water droplet + ice crystal) or glaciated phase (shades of yellow to green). By 2100 UTC (4:00 PM EST) on 18 November, the highest snowfall accumulations had reached 54 inches at Orchard Park just south of Buffalo.

A toggle between Suomi-NPP VIIRS True Color RGB and False Color RGB images (below) depicted the broad LES band at 1828 UTC. The VIIRS data used to create these images were acquired and processed using the SSEC/CIMSS Direct Broadcast ground station.

The GOES-16 Cloud Top Phase derived product (below) confirmed the Mixed Phase nature (darker green) of much of the LES band.

After sunset on 18 November, GOES-16 Infrared images (below) showed that while the Lake Erie LES band was not as well-defined as earlier in the day, it still persisted (with isolated brief periods of lightning). By 0000 UTC on 19 November (7 PM EST on 18 November), snowfall accumulations had reached 66 inches at Orchard Park.

===== 19 November Update =====

Breaks in the cloud cover on the morning of 19 November provided a partial glimpse of the snow cover that resulted from this LES event — appearing as shades of red in the GOES-16 Day Snow-Fog RGB images, and shades of green in the Day Cloud Phase Distinction RGB images — which extended into the western Finger Lakes region of western New York. Snowfall accumulations were as high as 77.0 inches at Orchard Park.

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A list of storm total snowfall amounts from this multi-day (16-20 November) LES event is available here.

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The Gálvez-Davison index (GDI) is a useful derived product (available online here from NCEP) for predicting tropical convection in tradewind regimes. In general, the larger the GDI, the more likely that showers and thunderstorms will be present. The toggle above compares GDI with K-Index derived from NUCAPS data (the Sounding Availability plot shows in green where the NUCAPS retrieval completed successfully; i.e., where the data are considered most appropriate). There’s good spatial correlation between the GDI and the diagnosed stability.

The toggle below compares GDI from GFS to the GOES-17 K-index (a Level 2 GOES-R derived stability product computed in clear skies only) that is displayed on top of GOES-17 Band 13 Clean Window infrared (10.3 um) imagery. A corridor of lower stability is readily apparent in the K Index, stretching across the Samoan Islands. Relatively stable air overlays Fiji, and a large region of instability lies east of American Samoa. The GDI agrees very well with the K Index derived from the satellite data, and convection as diagnosed by the Band 13 imagery is limited to regions where GDI values are larger. It is a good practice is to compare the K Index and the GDI. If the K Index starts to diverge from the predicted GDI, it’s a good idea to ask why!

Thanks to Dora Meredith and Elinor Lutu-McMoore, WGO PPG, for their assistance with this blog post!

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