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.

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.

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GOES-17 “Red” Visible (0.64 µm) images [click to play animated GIF | MP4]

GOES-17 (GOES-West) “Red” Visible (0.64 µm) images (above) showed the motion of pack ice away from the southwest coast of Alaska on 20 November 2021. Strong offshore winds (gusting in excess of 30 knots at times) were causing the ice motion away from the coast into the Bering Sea — although some landfast ice was also evident.

During the subsequent nighttime hours, a sequence of 3 Suomi-NPP VIIRS Day/Night Band (0.7 µm) images (below) allowed sea ice motion to be monitored during the long night, when GOES-17 Visible imagery was not available — providing that there is ample illumination from the Moon, which there was in this case (since it was in the Waning Gibbous phase, at 99% of Full).

Suomi-NPP VIIRS Day/Night Band (0.7 µm) images [click to enlarge]

This ice growth was being promoted by colder than normal Sea Surface Temperatures in that portion of the Bering Sea — along with the offshore flow of very cold air that had been in place across much of Alaska during the previous days. In fact, Interior Alaska recorded its first -40ºF/-40ºC temperatures of the winter season on this day.

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NOAA-20 Day Night Band imagery from early morning (from the VIIRS Today website at CIMSS) on 19 November 2021 shows the impact of the total lunar eclipse on Day Night Band imagery. A lunar eclipse will always occur during a Full Moon; ample lunar illumination off the east coast accompanied that descending NOAA-20 pass between about 0635 and 0640 UTC (as shown in this orbital path image from this website) — just as the lunar eclipse was starting. By the time NOAA-20 overflew the central US (0815 to 0825 UTC), near totality was occurring. The overflight on the west coast (0955 to 1005 UTC) occurred as the eclipse was starting to wane, so a bit more lunar illumination was available.

A similar Day Night Band image from Suomi-NPP (also from the VIIRS Today website) is below, and it shows similar differences in swath illumination. The west coast overpass by Suomi-NPP occurred around 1050 UTC; by then the eclipse had ended. Suomi NPP Day Night Band imagery is also available at the NASA Worldview site.

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Cold temperatures over the Great Lakes (the Green Bay 850-mb sounding temperatures, for example, were -11.9º C at 0000 UTC and -8.1º C at 1200 UTC) on 18-19 November 2021 generated extensive cloud cover and occasional snow showers. The nighttime microphysics RGB, above, showed extensive stratiform clouds downwind of Lake Michigan. The GOES-16 Cloud Top Phase product at this time (here) uniformly showed water droplets over lower Michigan.

Skies over eastern Lake Michigan were clear enough at 0811 UTC to allow some lake-surface temperature computations using NOAA-20 VIIRS channels and the ACSPO algorithm. Diagnosed lake-surface temperatures are in the low 50s (º Fahrenheit; the colorbar range is 40º to 55º F).

NUCAPS Sounding Availability points are also shown in the image above, and three points near/over western Lake Michigan — near Escanaba MI, east of Sheboygan WI, and near Racine, WI — are shown below.

NUCAPS profiles on this day over western Lake Michigan were uniformly warmer than 2-hour forecast NAM Nest profiles from the 0600 UTC model run. The image below compares (via Sharppy, using the NUCAPS in the Cloud methodology outlined here) model (red and green lines) and NUCAPS (purple lines) near Escanaba. NUCAPS shows warmer low-level temperatures (and also cooler mid/upper-tropospheric temperatures). Comparisons for the NUCAPS point east of Sheboygan and NAM Nest values at Sheboygan, and the NUCAPS point near Racine and the NAM Nest values at Milwaukee (note that for those two linked plots, the colors are flipped: NUCAPS Temperature and Dewpoint are red and green, respectively, and NAM Nest values are purple) are similar: NUCAPS values in the lowest part of the atmosphere are warmer than the NAM Nest. How might that affect the downwind production of lake-effect clouds and precipitation?

NUCAPS profiles give a model-independent estimate of temperature and dewpoint in the atmosphere. They are available about halfway (in time) between routine upper-air soundings in the lower 48 states of the USA. ‘NUCAPS Sounding Availability’ can be found under the ‘Satellite > S-NPP and NOAA-20’ menu.

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