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April 22, 2023: Happy Earth Day! The first Earth Day was celebrated on April 22, 1970. The idea was conceived by then Wisconsin Senator Gaylord Nelson and an estimated 20 million Americans participated on that first day, which was approximately 10% of the US population back then. You can read... Read More
April 22, 2023: Happy Earth Day! The first Earth Day was celebrated on April 22, 1970. The idea was conceived by then Wisconsin Senator Gaylord Nelson and an estimated 20 million Americans participated on that first day, which was approximately 10% of the US population back then. You can read more about the first Earth Day celebration on the Nelson Institute’s web page: Tracing Earth Day’s Origins.
Earth Day is a great excuse for us to show off some true color imagery from GOES! In case anyone forgot, you live on a beautiful planet! Do what you can to help keep it that way. Here’s a selfie of almost everyone in North and South America today at 17:00 UTC from GOES-16 (GOES-East) ABI:
(Click to Enlarge)
If you weren’t in that view, maybe you’re in this next one from GOES-18 (GOES-West) today at 21:00 UTC?
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I am attempting to get everyone in this next image. Unless you were off planet or in the Arctic or Antarctic Circle, you must be somewhere in this next image. A true color, local-noon composite from five geostationary imagers, thanks to the SSEC Satellite Data Services (SDS), here you are:
(Click to Enlarge)
Back in 2020, @GOESguy on Twitter shared this loop, starting with the first Earth Day in 1970 and every 10 years to 2020:
Or just for the Tweet on the first Earth Day in 1970, as seen by ATS III, here’s another @GOESguy tweet from 2022. Earth Day was started by a Wisconsinite (Nelson) and so was geostationary satellite meteorology (Vern Suomi). We continue to follow in these giant’s footsteps at both the Nelson Institute and SSEC.
5-minute CONUS sector GOES-16 (GOES-East) True Color RGB images from the CSPP GeoSphere site (above) showed the northward transport of smoke from the 35,000-acre Great Lake Fire in eastern North Carolina on 21 April 2023.In a closer view using 1-minute Mesoscale Domain Sector GOES-16 True Color RGB images (below), intermittent brighter white pyrocumulus clouds were... Read More
5-minute GOES-16 True Color RGB images [click to play animated GIF | MP4]
5-minute CONUS sector GOES-16 (GOES-East) True Color RGB images from the CSPP GeoSphere site (above) showed the northward transport of smoke from the 35,000-acre Great Lake Fire in eastern North Carolina on 21 April 2023.
In a closer view using 1-minute Mesoscale Domain Sector GOES-16 True Color RGB images (below), intermittent brighter white pyrocumulus clouds were seen in the immediate vicinity of the fire source.
1-minute GOES-16 True Color RGB images [click to play animated GIF | MP4]
1-minute GOES-16 Shortwave Infrared (3.9 µm) and Day Land Cloud Fire RGB images along with the Fire Power and Fire Temperature derived products (below) showed thermal signatures of the fire (the Fire Power and Fire Temperature products are components of the Fire Detection and Characterization Algorithm). 3.9 µm infrared brightness temperatures occasionally reached 137.71ºC (the saturation temperature of GOES-16 ABI Band 7 detectors), as early as 1524 UTC and as late as 2143 UTC.
GOES-16 Shortwave Infrared (3.9 µm, top left), Day Land Cloud Fire RGB (top right), Fire Power product (bottom left) and Fire Temperature product (bottom right) [click to play animated GIF | MP4]
The mp4 animation above shows the development of Tropical Storm Sanvu out of the Monsoon Trough in the western Pacific ocean on 19-20 April 2023. The ‘Pacific Tropical Airmass RGB’ used in the animation is described in this blog post, and it gives more information about cloud-top features compared to the Airmass RGB... Read More
Pacific Tropical Air Mass RGB, 1200 UTC on 17 April 2023 – 0000 UTC 21 April 2023
The mp4 animation above shows the development of Tropical Storm Sanvu out of the Monsoon Trough in the western Pacific ocean on 19-20 April 2023. The ‘Pacific Tropical Airmass RGB’ used in the animation is described in this blog post, and it gives more information about cloud-top features compared to the Airmass RGB (a similar mp4 animation for the airmass RGB is here). An interesting (and suggestive) aspect of the animation is the development of strong outflow to the north of the Monsoon Trough after 18 April that continues through the end of the animation.
MIMIC Total Precipitable Water fields — that incorporate wind information from the GFS — also show the development of a circulation as the system develops. Note that the storm is near the northern edge of the moist trough; the abundant dry air to its north might affect the future strength of the system.
MIMIC Total Precipitable Water fields, 1000 UTC on 17 April through 1200 UTC on 21 April 2023 (Click to enlarge)
Gridded NUCAPS fields (source) can also give views of the environment that contains the developing tropical cyclone. The three toggles below show the evolution from ca. 0300 UTC on 19 April through ca. 0230 UTC on 21 April 2023. Mid-level Total (850-700mb) Precipitable Water, Relative Humidity at 700 mb, and the 850-700 mb Lapse Rate all testify to the hostile environment (dry and stable) to the north of this developing system.
850-700 mb Precipitable Water diagnosed by gridded NUCAPS fields, ca. 0230-0300 UTC on 19, 20 and 21 April 2023 (click to enlarge)700 mb Relative Humidity diagnosed by gridded NUCAPS fields, ca. 0230-0300 UTC on 19, 20 and 21 April 2023 (click to enlarge)850-700 mb Lapse Rate diagnosed by gridded NUCAPS fields, ca. 0230-0300 UTC on 19, 20 and 21 April 2023 (click to enlarge)
Diagnostics from the SSEC/CIMSS Tropical Weather website for the storm, below, show the system forecast to move towards a region where higher shear now exists.
Predicted path of Sanvu, along with a 1200 UTC analysis of 200-850 mb wind shear and SSTs. Also shown: the 1420 UTC image of WV-IR brightness temperature differences, used to diagnose regions of overshooting tops (red to very dark red in the enhancement)
SATCON analysis of peak winds with Sanvu, 19 April through 21 April 2023 (Click to enlarge)
SATCON diagnostics from CIMSS, above, show that an initial period of strengthening has leveled off.The forecast from the Joint Typhoon Warning Center takes Sanvu towards the southern Marianas islands as a system with weak winds (but likely abundant moisture), as shown below.
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Predicted path of Sanvu, 1200 UTC on 21 April through 1200 UTC on 25 April, 2023 (Click to enlarge)
More information on this system is available at the webpages of the National Weather Service in Guam (link).
There was a conditional outlook for severe storms in Oklahoma on April 19th, owing to uncertainty in the forcing for ascent and a stout warm-sector cap. Well, the cap broke, and mayhem ensued in central Oklahoma. A break in the cirrus deck in southwest Oklahoma likely contributed to surface heating... Read More
There was a conditional outlook for severe storms in Oklahoma on April 19th, owing to uncertainty in the forcing for ascent and a stout warm-sector cap. Well, the cap broke, and mayhem ensued in central Oklahoma.
A break in the cirrus deck in southwest Oklahoma likely contributed to surface heating and enhanced mixing of the boundary layer, allowing the cap to break. ProbSevere LightningCast, an AI model that uses solely images of ABI data to predict next-hour lightning at any given point, was able to capture lightning initiation (Figure 1). Glaciating cloud tops are a prime signal of imminent lightning, and the one-minute scans from the GOES-16 mesoscale sector helped the model maximize lead time (Figure 2).
Figure 1: ProbSevere LightningCast contours, GOES-16 ABI day-cloud-land RGB, and GOES-16 GLM flash-extent density.
Figure 2: Electrified storms with annotations of lead time to lightning initiation or forecasts produced by LightningCast
ProbSevere version 3, a set of machine-learning models that predicts probabilities of severe weather hazards in the near future, was able to track these storms as they became severe (Figure 3). These storms produced huge hail (up to 3″ in diameter), straight-line winds exceeding 80 mph, and deadly tornadoes (Figure 4). One aspect of ProbSevere’s automated guidance that forecasters have frequently reported is that during busy situations, ProbSevere helps them quickly triage which storms or threats to prioritize and investigate further. In this outbreak, most storms had at least severe thunderstorm warnings, while several were tornado-warned. Both ProbSevere v3 and LightningCast will be evaluated by forecasters at NOAA’s Hazardous Weather Testbed this spring.
Figure 3: ProbSevere v3 storm contours (inner contour is colored by the probability of any severe; outer contour is colored by the probability of tornado), MRMS MergedReflectivity, and NWS severe weather warnings.
Figure 4: Preliminary storm reports from the Storm Prediction Center.
Just a little further north in Kansas, LightningCast was able to correctly predict lightning initiation (with about 16 minutes of lead time) for the storm despite moderate overlapping cirrus clouds (Figure 5). Despite the obscuring ice clouds, LightningCast was able to discern elevated lightning potential with the aid of the visible red band (0.64-µm reflectance), snow-ice band (1.6-µm reflectance), and long-wave infrared bands (10.3-µm and 12.3-µm brightness temperatures) and the spatial patterns evident in the growing cumuliform clouds.
Figure 5: ProbSevere LightningCast contours, GOES-16 ABI day-cloud-phase-distinction RGB, and GOES-16 GLM flash-extent density.
In fact, for this storm, the reflective bands were quite important. When they were removed from the model (a “data-denial” experiment), the probability of lightning was < 10% for this convection at 21:25 UTC, whereas the full LightningCast model produced a maximum probability of about 50% at this time (Figure 6). While we have found that LightningCast performs quite well at night (when the reflective bands are uniformly zero), moderate cirrus cover at night might be an instance where users should expect less lead time to lightning initiation.
Figure 6: LightningCast probabilities for the full model (“control”, left) and the model without the reflective bands (right). The background image is the day-cloud-phase-distinction RGB.
Notice how difficult it is to visually pick out growing convection in this scene, with only the long-wave infrared bands plotted. LightningCast can only discern what humans are able to pick out in the satellite imagery, but LightningCast does it quickly, automatically, objectively, and without ceasing, aiding forecasters in their decision making.
Figure 7: Predictions made by the LightningCast model without the reflective bands. The background image is an “infrared cloud phase” RGB from GOES-16, used to help discern cloud phase at nighttime.
