Suomi-NPP VIIRS Visible (0.64 µm) and Shortwave Infrared (3.74 µm) images [click to play animated GIF | MP4]
A sequence of Suomi-NPP VIIRS Visible (0.64 µm) and Shortwave Infrared (3.74 µm) images (above) revealed a long east-to-west oriented swath of horizontal convective roll (HCR) clouds associated with blowing snow and blizzard conditions across parts of the North Slope of Alaska on 15 April 2022. The plume of supercooled water droplet HCR clouds appeared warmer — lighter shades of cyan — due to enhanced reflection of incoming solar radiation At reporting sites within the northern portion of the HCR clouds and blowing snow, winds were gusting in the 35-40 knot range and the visibility was often 1/2 to 1/4 mile.
A Suomi-NPP VIIRS SnowCloud RGB image at 1624 UTC (below) showed that this plume of HCR cloud features — which was mixed with blowing snow — crossed the coast of northwestern Alaska and extended several miles westward across nearshore waters of the Chukchi Sea.
Suomi-NPP VIIRS SnowCloud RGB image at 1624 UTC (credit: Jason Ahsenmacher, NWS Fairbanks) [click to enlarge]
GOES-17 Near-Infrared Snow/Ice (1.61 µm) images created using Geo2Grid(below) showed how the HCR cloud plume evolved during the day.
GOES-17 Near-Infrared Snow/Ice (1.61 µm) images [click to play animated GIF | MP4]
Thanks to Jason Ahsenmacher, NWS Fairbanks, for bringing this interesting case to our attention!
NOAA-20 I05 (11.45 µm) imagery over the Arctic Ocean, 1455-1510 UTC on 1 February 2022 (Click to — greatly!! — enlarge)
Previous blog posts (example) have documented how to create imagery from the VIIRS instrument, and this one is another example. For example, if you receive a request for VIIRS imagery such as this one: “If you have time to try a case, you could pick Feb 1, 2022 near North Pole Point for 11 um channel” — how do you proceed? A first step is to determine the day/time of the data, and that’s achieved by looking at orbits over the Arctic, at this website. That website also has an archive, and the archive for Arctic passes on 1 February 2022 is here. The image shows NOAA-20 passing over Greenland, the Arctic Ocean, and then moving over eastern Russia between 1455 and 1510 UTC on 1 February 2022. Now you know the times to request.
NOAA CLASS is the data repository that stores VIIRS imagery from NOAA-20 and Suomi-NPP. Go to the website, an log in (register if you have not already), and choose JPSS VIIRS SDRs (Operational Sensor Data Records) as shown below. Choosing those data and clicking >>GO to the right of the drop-down menu bar will move you to a new data-selection menu, where you will select the day/time of the data (1 February 2022, 14:55 Start time, 15:10 end time), and the band (I chose SVI05 — the 11.45 µm Imager channel, with 375-m resolution, that is: VIIRS Imagery Band 05 SDR (SVI05) (public 02/07/2012) ), and the satellite (NOAA-20). Geolocation data must also be selected, and Polar2Grid will expect the GITCO files. Choose them ( VIIRS Image Bands SDR Ellipsoid Terrain Corrected Geolocation (GITCO) (public 02/07/2012)) as well. It is very important, however, that your User Preferences are configured so that the data are disaggregated! Click on User Preferences, and make that selection. The User Preferences page should include information as shown here.
NOAA CLASS front page showing the VIIRS data to select (Click to enlarge)
The steps above will produce 12 matches — 6 files of SVI05 and 6 GITCO files. Submit your order and wait for the email notification that the files are ready. While you are waiting, if you’ve not done so already, download the Polar2Grid software from CIMSS CSPP Site (CSPP: Community Satellite Processing Package; note that a free registration might be required). Expand the downloaded (compressed tar) file into an empty directory, and enter this unix command: export POLAR2GRID_HOME=/directory/where/the/expanded/file/sits.
NOAA CLASS will send an email once the data are staged and ready for you. Download those data, and then enter this command:
That code takes the viirs_sdr and GITCO data pointed to by the -f flag and creates a geotiff of i05 (11.45) imagery. Because these data are near the Pole, I’ve specified a grid (‘-g polar_canada’) to be used (a full list of pre-defined grids is here, part of the Polar2Grid online documentation). The command will stitch together the data in the 6 different files, and you’ll see an image at full resolution, as shown above. I also used software to add coastlines and lat/lon (‘grid’) lines.
A colder-than-normal February over the western Great Lakes (through the 27th, Duluth is 10o F below normal; Marquette is 5o F below normal; Green Day is 2o F below normal; Cleveland is 1o F below normal) has fostered a growth in ice cover over the Lakes (This figure, from here, for example). How can satellites detect that ice, especially for a region where winter-time cloudiness is notorious? In general, there are two different ways to detect ice: Visible/Infrared imagery and Microwave imagery. The toggle above shows Advanced Technology Microwave Sounder (ATMS) ice detection (using MiRS algorithms and data from Suomi-NPP (1807 UTC) and NOAA-20 (1859 UTC)) over the Great Lakes on 25 February 2022. (These data come from the Direct Broadcast antenna at CIMSS, and are processed using CSPP to produce AWIPS-ready tiles that are available via an LDM feed). A big challenge with this field is the very large ATMS footprint. Note that these sea-ice concentration values are quantitative: values change based on how much ice is within the footprint but are also dependent on view angle.
VIIRS data (as shown at the VIIRS Today website, for example) can also be used to infer regions of ice in a qualitative sense, as shown below. The true color imagery shows possible ice features over the lakes. It’s a challenge, however, to use a single VIIRS image to distinguish (mostly) stationary ice from (usually) moving clouds. Multispectral VIIRS imagery means (via the use below of the 2.25 µm M11 band) ice features are cyan colored and can be qualitatively distinguished from clouds. Consider, for example, the color difference in the False Color image between the clouds over eastern Lake Superior (white in both True- and False-Color) and near-shore ice over southern and eastern Lake Superior (white in the True-Color and cyan in the False-Color). There are also VIIRS-based Ice Concentration products that can be computed in clear skies.
Suomi-NPP VIIRS True- and False-Color imagery over the Great Lakes, 1806 UTC on 25 February 2022 (Click to enlarge)
For cloudy regions, Advanced Baseline Imagery can be used to create estimates of Ice Surface Temperature and Ice Concentration. Quantitative estimates such as these give more information than the qualitative estimates shown above. These estimates at present are only created hourly; in partly-cloudy regions, that cadence is sufficient to give lake-wide quantitative estimates of ice coverage and temperature. CONUS imagery — with a 5-minute time cadence — and mesoscale sectors — with 1-minute cadences — can be used to monitor (in a qualitative sense) how ice is moving (as shown link, for example). High temporal-resolution imagery is important because ice sheets can change rapidly under strong winds, as shown in this tweet. At high latitudes, there are also ways of monitoring ice motion via polar orbiters (link).
GOES-16 Estimates of Ice Surface Temperatures, 1800 and 1900 UTC on 25 February 2022 (Click to enlarge)GOES-16 Estimates of Ice Concentration, 1800 and 1900 UTC on 25 February 2022 (Click to enlarge)
A much higher-resolution method of viewing ice (again, in a qualitative, not quantitative sense) in regions of both clear and cloudy skies, day or night, is through the use of Synthetic Aperture Radar (SAR). Data are available for each Great Lake at this link. Imagery for each Lake over the past days is available there, albeit infrequently (usually around 0000 and 1200 UTC only) and over small domains. This qualitative imagery, however, is very high-resolution and gives very impressive details. Imagery over Lake Erie is shown below.
RCM estimates of Lake Erie Ice, 24-27 February 2022 (Click to — greatly! — enlarge)
VIIRS and ABI give both qualitative (false-color, visible imagery) and quantitative (ice concentration and ice temperature) depictions of ice over the lakes — or over coastal waters around Alaska. Microwave data also gives quantitative estimates (ice concentrations with large microwave footprints) and qualitative estimates (SAR data). Use all products to create a clear picture of ice coverage.
GOES-16 Ice Concentration, 1300 UTC on 4 February 2022 (click to enlarge)
GOES-16 Cryosphere products — Ice Mask, Ice Concentration and Ice Surface Temperature — have been developed and are in a testing phase. The AWIPS screenshot above shows ice concentrations over parts of Lakes Superior, Huron and Erie. These products are created over the Full Disk ABI domain in regions of clear sky only (clouds over Lakes Michigan and Ontario mean Ice Concentration is not computed there at 1300 UTC). The Ice Mask at the same time, below, shows ‘Day Ice’ (cyan) and ‘Night Ice’ (green) flags; cryosphere products are created day and night, but different algorithms are used: Bands 14 and 15 (11.2 µm and 12.2 µm, respectively) are used day and night; the daytime product also uses Bands 2, 3 and 5 (at 0.64 µm, 0.86 µm and 1.61 µm, respectively) as noted in the Advanced Theoretical Basis Document — ATBD — here.
GOES-16 Ice Mask, 1300 UTC on 14 February 2022 (Click to enlarge)
The strength of ABI is frequency (every hour) of observations, so an animation, as shown below, will allow a user multiple views of the lakes; in partly cloudy (or clearing) conditions, values from one hour can augment values from other hours. An example is shown below with Ice Concentration over the Great Lakes. Clearing skies over Lake Erie allow for a more complete description of Lake Ice, whereas increasing clouds over western Ontario and western Lake Superior mean GOES-R Cryosphere observations are lost. In the absence of very strong winds, however, lake ice does not erode quickly, so although the observations are precluded by cloud cover, earlier observations likely remain valid. It is important to know where the clouds are, however, when viewing these products.
GOES-16 Sea Ice Concentration, hourly from 0700-1600 UTC on 14 February 2022 (Click to enlarge)
The utility of these clear-sky products with good temporal frequency is augmented in combination with all-sky products that give observations only once or twice daily — such as Synthetic Aperture Radar (SAR) ice observations that available here. For example, the 2307 UTC 13 February Normalized Radar Cross Section (NRCS) image (below) over eastern Lake Erie can be used to fill in information not available from the GOES-16 Cryosphere product.
RADARSat Constellation Mission (RCM)-3 NRCS imagery over Lake Ontario and eastern Lake Erie, 2307 UTC on 13 February 2022 (Click to enlarge)
The AWIPS toggle below shows Ice Surface Temperature plotted below and on top of the Binary Cloud Mask. You will note that there are regions where Ice Surface Temperature is computed in regions where the Binary Cloud Mask shows clouds! How can this occur in Clear Sky products such as the Cryosphere products (a similar toggle could be created with Ice Concentration or Ice Mask)?
The Cloud Mask in AWIPS shows regions that are Clear or Cloudy — it is binary. The Cloud Mask actually has 4 different states (as noted here): Clear, Probably Clear, Probably Cloudy and Cloudy. The Cloud Mask in AWIPS assigns ‘Cloudy’ to all pixels that aren’t Clear: that includes ‘Probably Clear’, ‘Probably Cloudy’ and ‘Cloudy’ pixels. In contrast, the Cryosphere products are produced in regions that are both Clear, or Probably Clear (as noted in the ATBD). That’s why Cryosphere products can show up in regions that AWIPS shows are Cloudy.
GOES-16 Cryosphere Ice Surface Temperature and GOES-16 Cloud Mask, 1300 UTC on 14 February 2022 (Click to enlarge)
