JMA Himawari-8 “Clean” Infrared Window (10.4 µm) images (above) displayed the Fujwhara effect between Cyclone Vernon (which rapidly intensified and briefly developed a small eye) and Tropical Invest 93S, as they rotated (clockwise) around each other in the South Indian Ocean — west of Cocos Island, station identifier YPCC — during the 25 February – 27 February 2022 period. Invest 93S was eventually absorbed by Vernon (which decreased in intensity during the absorption  process).

Himawari-8 “Red” Visible (0.64 µm) images (below) showed Vernon when it had reached a peak intensity of Category 3 (ADT | SATCON) — however, the eye remained cloud-filled during that time.

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A colder-than-normal February over the western Great Lakes (through the 27th, Duluth is 10^o F below normal; Marquette is 5^o F below normal; Green Day is 2^o F below normal; Cleveland is 1^o 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.

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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).

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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.

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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.

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LightningCast (available here) is a CIMSS-developed product (created at the request of OPC and WPC) designed to relate current ABI fields (Band 2 (0.64 µm), 5 (1.61 µm), 13 (10.3 µm) and 15 (12.3 µm)) to the likelihood of future lightning. (A short training video on this product is available here) In the animation above, probability contours are shown (blue: 10%; cyan: 25%; green: 50%; magenta: 75%) for the 45 minutes between 1731 and 1816 UTC, overlain on a sandwich-type RGB that includes visible imagery and enhanced Band 13 imagery for cold cloud tops. Of particular interest for this blog post is the region that develops east of Delmarva (as indicated in this image at 1751 UTC); probabilities increase there starting at 1741 and the first lightning is observed by GLM at 1756 UTC and it becomes more constant by 1806 UTC.

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The GOES-16 Band 13 imagery at 1701 UTC — 45 minutes before the onset of the LightningCast — and derived Lifted Index shows convection approaching a ribbon of lower stability (show in yellow) to the east of North Carolina. That stability product is suggesting: this is where convection is most likely to strengthen because stability is lower here. Then 45 minutes later, the LightningCast probability increases and lightning is observed!

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As noted in this blog post, some Level 2 products from GOES-R are clear sky only. The timing of this event was such that NUCAPS profiles could be used to fill in information where clouds obscure what is occurring (GOES level 2 stability product-wise). A challenge is that NUCAPS and GOES Stability products don’t match well (that is, it’s not easy to create a NUCAPS Lifted Index). The Total Totals Index derived from NUCAPS is shown below. (This figure shows the Retrieval Status of the profiles used). A corridor of instability is present where lightning-produced convection subsequently developed.

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Use GOES-R Stability indices and NUCAPS stability indices to identify regions where LightningCast might be needed in the future to highlight where lightning might occur. The synthesis of the different products is very helpful to creating the best forecast. AWIPS imagery in this blog post was created using the NOAA/NESDIS TOWR-S AWIPS Cloud Instance. Thank you!

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GOES-16 (GOES-East) Day Snow-Fog RGB and Day Cloud Phase Distinction RGB images (above) showed numerous industrial plumes — likely from power plants — streaming southward over parts of Texas on 24 February 2022. Particles emitted from power plant stacks acted to “seed” the surrounding supercooled water cloud droplets, creating a higher concentration of smaller cloud droplets — which were more efficient reflectors of incoming solar radiation, making the plumes appear brighter. Farther to the east, the tightly-spaced cloud bands of an undular bore were also apparent (1901 UTC image).

A higher-resolution view of these features was provided by a sequence of NOAA-20 VIIRS True Color RGB, False Color RGB, Visible and Near-Infrared images (below).

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