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Cold air drainage into the valleys of the Yukon

As an area of arctic high pressure settled over the Yukon region of northwestern Canada on 29 November 2012, strong radiational cooling led to very cold surface air temperatures (-49º F at Mayo, station identifier CYMA) and drainage of this cold, dense air into the valleys and lower elevations. An AWIPS image... Read More

Suomi NPP VIIRS 11.45 µm IR image + METAR surface reports

Suomi NPP VIIRS 11.45 µm IR image + METAR surface reports

As an area of arctic high pressure settled over the Yukon region of northwestern Canada on 29 November 2012, strong radiational cooling led to very cold surface air temperatures (-49º F at Mayo, station identifier CYMA) and drainage of this cold, dense air into the valleys and lower elevations. An AWIPS image of Suomi NPP VIIRS 11.45 µm IR channel data with an overlay of METAR surface reports (above) showed the dendritic pattern of cold air drainage into the valleys (darker blue color enhancement); the coldest IR brightness temperature on the image was -51º C (violet color enhancement).

Even though fog and freezing fog was being reported at a few of the surface stations, a comparison of the Suomi NPP VIIRS 11.45 µm IR, 0.7 µm Day/Night Band, and the 11.45-3.74 µm “fog/stratus product” images (below) indicated that not all of the valley fog features could be easily seen — in particular, most of the areas of shallow ice fog did not exhibit a signal on the Day/Night Band or the fog/stratus product IR brightness temperature difference images.

Suomi NPP VIIRS 11.45 µm IR, 0.7 µm Day/Night Band, and 11.45-3.74 µm "fog/stratus product"

Suomi NPP VIIRS 11.45 µm IR, 0.7 µm Day/Night Band, and 11.45-3.74 µm “fog/stratus product”

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Strong Tornado in southern Italy

A rare November tornado moved through Taranto, (YouTube video, AccuWeather blog entry) in southern Italy, on Wednesday November 28th. A loop of 10.8 µm Meteosat-9 imagery (above) shows the development of an overshooting top in a thunderstorm that is moving over Taranto between 0900 and 0915 UTC (Note that the time indicated on... Read More

Meteosat-9 10.8 µm infrared channel images (click image to play animation)

Meteosat-9 10.8 µm infrared channel images (click image to play animation)

A rare November tornado moved through Taranto, (YouTube video, AccuWeather blog entry) in southern Italy, on Wednesday November 28th. A loop of 10.8 µm Meteosat-9 imagery (above) shows the development of an overshooting top in a thunderstorm that is moving over Taranto between 0900 and 0915 UTC (Note that the time indicated on the satellite image is the nominal time — the time that the satellite starts scanning. The actual scan time over southern Italy is approximately 10 minutes later than the nominal time). Such cloud-top features are frequently associated with severe weather. A faint suggestion of an enhanced-V/thermal couplet is apparent in the later imagery as the strong thunderstorm moves northward across the Salento peninsula and then into the Adriatic Sea. METOP-A infrared imagery (below) shows the thunderstorm complex about an hour before a tornadic storm moved inland from the Ionian Sea. The corresponding Meteosat-9 image is here. The higher spatial resolution of the polar orbiter METOP-A allows the discernment of much finer detail in the cloud-top features.

METOP-A 10.8 µm IR imagery

METOP-A 10.8 µm IR imagery

Meteosat-9 0.6 µm visible channel images (click image to play animation)

Meteosat-9 0.6 µm visible channel images (click image to play animation)

Visible imagery from Meteosat-9, above, also shows the development of the overshooting top associated with the tornadic cell. A higher-resolution visible imager from METOP-B, below, showed the line of thunderstorms in which the tornadic cell, indicated by the yellow arrow, was embedded.

METOP-B 0.63 µm Visible imagery

METOP-B 0.63 µm Visible imagery

The tornadic weather was associated with an exceptionally deep extratropical cyclone. On Monday, that system was over the northwestern Mediterranean (see below), with ample evidence of exceptionally cold upper-level air over the Bay of Biscay. This storm also had a history of producing supercellular thunderstorms, as evidenced by the storm development just south of France in the animation below.

Meteosat-9 Visible imagery (0.6 µm) (click image to play animation)

Meteosat-9 Visible imagery (0.6 µm) (click image to play animation)

A multi-day loop of the Meteosat-9 infrared window channel imagery is below. It shows the strong extratropical cyclone moving across southern Europe.

Meteosat-9 10.8 µm infrared channel images (click image to play animation)

Meteosat-9 10.8 µm infrared channel images (click image to play animation)

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Unusual pattern of cloud edge clearing

Hat tip to Chad Gravelle (CIMSS), who asked about the curious pattern of trailing edge cloud clearing across South Carolina on the morning of 28 November 2012. Given that the etiology of these elongated cloud clearing line features is unknown at this point,... Read More

GOES-13 0.63 µm visible channel images (click image to play animation)

GOES-13 0.63 µm visible channel images (click image to play animation)

Hat tip to Chad Gravelle (CIMSS), who asked about the curious pattern of trailing edge cloud clearing across South Carolina on the morning of 28 November 2012. Given that the etiology of these elongated cloud clearing line features is unknown at this point, this case is a perfect candidate for the “What the heck is this?” blog category. An animation of GOES-13 0.63 µm visible channel images (above; click image to play animation) shows the unusual cloud edge clearing pattern moving southwestward across South Carolina.

These cloud features were also seen on an AWIPS image of POES AVHRR 0.63 µm visible channel data at 13:53 UTC (below). Surface observations showed that air with drier dew point values was being advected southwestward into the trailing cloud edge, but the wind speeds were generally light at most reporting sites — so the cause of the elongated “clear slot” features is unclear.

POES AVHRR 0.63 µm visible channel image

POES AVHRR 0.63 µm visible channel image

The three POES AVHRR products shown below indicated that these trailing edge cloud features were liquid water clouds, which exhibited cloud top temperature values of +1 to +3º C, with a cloud top height value of 2 km.

POES AVHRR Cloud Type product

POES AVHRR Cloud Type product

POES AVHRR Cloud Top Temperature product

POES AVHRR Cloud Top Temperature product

POES AVHRR Cloud Top Height product

POES AVHRR Cloud Top Height product

On the GOES-13 and POES AVHRR images, you can see some evidence of similar cloud edge clearing lines over the Alabama/Georgia border region. During the previous overnight hours, this cloud signature was very well-defined over Georgia, as seen on a Suomi NPP VIIRS 0.7 µm Day/Night Band “night-time visible image” at 06:43 UTC or 1:43 AM local time (below).

Suomi NPP VIIRS 0.7 µm Day/Night Band image

Suomi NPP VIIRS 0.7 µm Day/Night Band image

 

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Southeastern US: widespread fire activity, and a good view of the Gulf Stream

4-km resolution GOES-13 3.9 µm shortwave IR channel images (above; click image to play animation) detected a number of “hot spots” (black to yellow to red colored pixels) due to widespread small agricultural fires that were burning across parts of the southeastern US on Read More

GOES-13 3.9 µm shortwave IR images (click image to play animation)

GOES-13 3.9 µm shortwave IR images (click image to play animation)

4-km resolution GOES-13 3.9 µm shortwave IR channel images (above; click image to play animation) detected a number of “hot spots” (black to yellow to red colored pixels) due to widespread small agricultural fires that were burning across parts of the southeastern US on 26 November 2012. This type of burning is a common practice to clear debris from fields and prepare them for future planting of crops.

Suomi NPP VIIRS 3.74 µm shortwave IR image

Suomi NPP VIIRS 3.74 µm shortwave IR image

A 1-km resolution Suomi NPP VIIRS 3.74 µm shortwave IR image at 18:45 UTC (above) showed many more very small hot spots across the region. A comparison of this VIIRS image with the corresponding GOES-13 image (below) showed some interesting differences. First of all, the higher spatial resolution of the VIIRS data (375 meters, re-mapped onto a 1-km AWIPS grid) detected a greater number of the smaller fires, compared to the more coarse (4 km resolution) GOES data. Secondly, even though the image times were close (GOES-13 was scanning the region at 18:49 UTC, while the Suomi NPP satellite was passing over at 18:47 UTC), a couple  of the fires in Georgia exhibited hotter IR brightness temperatures on the GOES image. As can be inferred from the GOES animation, many of these fires were fairly short-lived, so GOES may have been scanning the area when those particular fires were flaring up to their largest size.

GOES-13 3.9 µm and Suomi NPP VIIRS 3.74 µm shortwave IR channel images

GOES-13 3.9 µm and Suomi NPP VIIRS 3.74 µm shortwave IR channel images

If you look at the adjacent offshore waters in the VIIRS and GOES shortwave IR images above, you can see the warmer signature (darker gray enhancement) of the Gulf Stream. A comparison of 1-km resolution MODIS Sea Surface Temperature (SST) product images at 15:22 UTC and 18:40 UTC (below) revealed some very intricate structure to the axis of the Gulf Stream, with a number of cold and warm eddy features. Note the very sharp SST gradient along the north wall of the Gulf Stream, off the coast of North Carolina: the SST values change from 60s F (green colors) to 70s F (orange colors) over a very short distance.

MODIS Sea Surface Temperature product images

MODIS Sea Surface Temperature product images

A comparison of the MODIS SST product with the Real-time, global, sea surface temperature (RTG_SST_HR) analysis (below) showed that the model was unable to resolve many of the smaller eddy features — so the model SST values were as much as 5-10 degrees F different in some of those locations.

MODIS Sea Surface Temperature + RTG_SST_HR model analysis

MODIS Sea Surface Temperature + RTG_SST_HR model analysis

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