The Great Lakes, viewed using GOES-16 and NOAA-20 imagery

January 21st, 2022 |

GOES-16 “Red” Visible (0.64 µm) images [click to play animated GIF | MP4]

1-minute Mesoscale Domain Sector GOES-16 (GOES-East) “Red” Visible (0.64 µm) images (above) displayed mesovortices over southern Lake Michigan on 21 January 2022. The formation of these mesovortex features was aided by a mid-lake convergence of surface winds, which was suggested by RAP40 surface wind fields and shown n more detail by Metop-C ASCAT winds from this site (below).

Metop-C ASCAT surface scatterometer winds [click to enlarge]

Farther to the north, in spite of a cold night across northeast Wisconsin and Upper Michigan — with morning low temperatures of -30ºF at Laona, Wisconsin and -39ºF at Amasa, Michigan — GOES-16 Visible images (below) showed that southerly winds helped to open an ice lead near the center of Green Bay, with a slow northward drift of pack ice in the northern half of the bay. A lone ice floe was also seen moving northward near the western edge of the clouds in Lake Michigan.

 GOES-16 “Red” Visible (0.64 µm) images [click to play animated GIF |MP4]

A toggle between NOAA-20 VIIRS True Color and False Color RGB images (below) revealed a more detailed view of the ice structure — and also showed the narrow southwest-to-northeast oriented damage path that remained from a June 2007 EF-3 tornado that went through a portion of Menominee, Langlade and Oconto counties. The higher spatial resolution of the VIIRS imagery helped to highlight the aforementioned isolated ice floe in Lake Michigan (which appeared as cyan in the False Color RGB image).

NOAA-20 VIIRS True Color and False Color RGB images [click to enlarge]

To the east, mesovortices were also observed in Lake Huron – long with ice floes drifting away from the coast of Lower Michigan (below).

GOES-16 “Red” Visible (0.64 µm) images [click to play animated GIF | MP4]

A was the case in Lake Michigan, these Lake Huron mesovortices were forming along an axis of surface wind convergence, seen in Metop-B ASCAT data (below).

Metop-B ASCAT surface scatterometer winds [click to enlarge]

A larger-scale toggle between NOAA-20 VIIRS True Color and False Color RGB images — created using data received from the SSEC/CIMSS Direct Broadcast ground station — provided a view of the entire Great Lakes region (below). 

NOAA-20 VIIRS True Color RGB and False Color RGB images (credit: Margaret Mooney, CIMSS) [click to play animation]

Strong gap flow into the Gulf of Tehuantepec

November 26th, 2021 |
GOES-16 True Color imagery, 1330 – 1520 UTC on 26 November 2021

GOES-16 True-Color imagery from the CSPP Geosphere site (link showing the data above) on 26 November, above, show features associated with strong flow through Chivela Pass in southern Mexico, gap winds often called Tehuano winds or Tehuantepecers. Strong descent associated with these events can often limit the presence of clouds that can be used as tracers. However, scatterometry (from this website) will show surface winds, and an MetopB overpass shortly after the end of the animation above, below, shows a core of strong winds over the ocean.

ASCAT Winds from Metop-B, 1532 UTC on 26 November 2021 (Click to enlarge)

The GOES-16 CONUS domain extends southward to the northern part of the Gulf of Tehuantepec (about 14.6 N Latitude). Visible imagery from 1516 UTC, below, is overlain with the Derived Motion Wind vectors (in the surface – 900 mb layer) at the same time. Strong northerly winds north of Chivela Pass are apparent, but the lack of clouds to track in the Gulf prevented the inference of winds there from the GOES-16 data.

GOES-16 Visible Imagery (Band 2, 0.64 µm) and Derived Motion Winds, surface-900 mb, 1516 UTC 26 November 2021 (Click to enlarge)

The strong winds are also associated with a local increase in Aerosol Optical Depth (AOD), as shown below.

GOES-16 Aerosol Optical Depth (AOD) at 1520 UTC on 26 November 2021 (click to enlarge)

Strong winds will cause significant mixing in the upper part of the ocean, which will result in cooling. Imagery from this website (shown below) shows cooling in the Gulf from previous events. Here is an animation from that website, courtesy Tim Schmit, NOAA/NESDIS/STAR

SST analysis valid at 24 November 2021 (Click to enlarge)

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GOES-17 True Color RGB images [click to play animated GIF | MP4]

In GOES-17 True Color images created using Geo2Grid (above), enhanced forward scattering during the morning hours helped to highlight the offshore transport of airborne dust.

Other blog posts discussing Tehuano wind events can be found here.

ASCAT winds versus SAR winds

November 22nd, 2021 |
ASCAT winds from Metop-B, 0616 and 1826 on 22 November 2021 (Click to enlarge)

From an email came this question: RADARSAT vs ASCAT winds, what are the differences between the two methods?

This comparison is not easy to make directly, as the orbits of Metop-B and Metop-C, the two satellites that carry the ASCAT instrument (now that Metop-A, which satellite also carried ASCAT, has been decomissioned), don’t sample the ocean at the same time/location as RADARSAT. The toggle above shows ASCAT winds from Metop-B (Metop-B orbits on 22 November 2021 are here, from this website) at 0631 and 1826 UTC on 22 November (from this source) in the region around Haida Gwaii (once known as the Queen Charlotte Islands). An obvious frontal passage occurred between those two times; this is also shown in the animation of surface charts (every 3 hours from 0600 through 1500 UTC shown here).

Imagery below shows SAR winds from RCM3 and RCM1 (RCM = RADARSAT Constellation Mission) at 02:23 (top) and around 15:03 (bottom) UTC on 22 November. The 15:03:52 image that follows the two images at bottom is here.

RCM3 RADARSAT SAR winds at 02:23 UTC on 22 November 2021 (Click to enlarge)
RCM1 RADARSAT SAR winds, 15:02:50 – 15:03:13 on 22 November 2021 (Click to enlarge)

How does scatterometery measure winds? If wind speeds over the ocean (or a lake) are very light, the water surface will be smooth. Microwave energy from a side-looking radar (ASCAT and SAR are both active radars; that is, they emit a ping and listen for a response) will reflect off it, and not scatter back to the instrument. As winds increase, small ripples develop and backscatter increases. Backscattered energy is greatest if the radar look and the wind direction are aligned; also, the backscatter is greater if the wind is blowing towards (vs. away from) the satellite. This is a source of ambiguity in direction. The backscatter distribution sensed has a name: Normalized Radar Cross-Section (NRCS); many different wind speed/direction combinations can produce the same NRCS. How can you mitigate these ambiguities?

For ASCAT instruments, the ambiguity is reduced through multiple measurements of the same surface — this gives NRCS values with different aspect and incident angles. Multiple measurements are achieved via the multiple antennas that are part of the ASCAT instrument (similarly, rotating beams on instruments such as AMSR-2 give multiple observations). Multiple observations allow for an accurate estimate of wind direction given the observations.

SAR processing mitigates the ambiguities by using numerical model output that suggests the correct wind direction. A challenge is that numerical model data has a far coarser resolution than SAR data. (Model data might also include errors!) As a result, artifacts can be introduced, and a good example is shown in the 02:23 UTC image above at 54.6 N, 131.8 W. In that region, where the windspeeds have an hourglass shape, the model wind direction is unlikely to be consistent with the observations. Keep that in mind when observing SAR winds.

One other aspect of the ASCAT v. SAR wind comparison bears notice: ASCAT winds have a typical upper bound, at around 45-50 knots. At stronger wind speeds, the backscatter to the ASCAT instrument is affected by foam on the sea surface that typically accompanies such strong winds. Special SAR wind processing (as discussed here) allows for observations of much stronger winds, as shown for 2020’s Hurricane Laura, where Seninel-1 SAR observations peaked at 150 knots! These computations use cross-polarization observations from SAR. Both SAR and ASCAT use co-polarization observations. Future ASCAT missions will support cross-polarization observations.

Some of the information above came from this link (specifically, here). If there are errors in the description, they’re this blogger’s fault however.

Goodbye* to Metop-A

November 15th, 2021 |
ASCAT data from Metop-A, ascending passes, 15 November 2021 (Click to enlarge)

The polar orbiting satellite Metop-A was switched off on 15 November 2021, ending a very long career gathering information (the satellite was launched in 2006). The final ASCAT (ascending) imagery from Metop-A, from this site, is shown above (click here to see the descending data from that day). ASCAT scatterometery from satellites operated by EUMETSAT will now be limited to Metop-B and Metop-C (Click here for more information on Metop-A in the near future; click here for information on Metop satellites). ASCAT data is obviously important for defining wind features over the open ocean, where conventional data is sparse.

The loss of Metop-A ASCAT data means a 50% reduction in the scatterometery data assimilated into US Numerical models. Only Metop-B ASCAT data are now assimilated; work continues on incorporating Metop-C ASCAT data into assimilation schemes.

Metop-A also carried AVHRR, IASI, HIRS, AMSU and MHS instruments. Metop-C is Metop-A’s replacement.


*Metop-A does continue to supply some (non-ASCAT) information to NESDIS (my thanks to Liam Gumley, SSEC, for this information!) as shown at this website. Not all instruments have (as yet) been switched off.