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ProbSevere in the Oklahoma severe-weather outbreak

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.

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A Lot of Satellites

There are more than 7300 satellites in orbit above the Earth, and the mp4 animation above (from Rick Kohrs, SSEC; click here for an animated gif) shows their locations during about 3 hours on 6 April 2023. The points were computed using TLE data from https://celestrak.org. Some satellites are in geostationary orbit along the equator... Read More

Computed Satellite Positions for 3 hours on 6 April 2023

There are more than 7300 satellites in orbit above the Earth, and the mp4 animation above (from Rick Kohrs, SSEC; click here for an animated gif) shows their locations during about 3 hours on 6 April 2023. The points were computed using TLE data from https://celestrak.org. Some satellites are in geostationary orbit along the equator and show little movement; various polar orbiters and starlink orbits are apparent as well. That’s a lot of satellites!

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More results from a numerical model input with Polar Hyperspectral Soundings fused with ABI data (Convective Weather edition)

Severe weather on 15 April and 19 April affords another opportunity to compare Significant Tornado Parameter predictions from a 4-km WRF simulation that includes information from Polar Hyperspectral Soundings and a 3-km HRRR simulation that does not. Consider the example, above, at 2200 and 2300 UTC on 15 April and 0000 and... Read More

Significant Tornado Parameter from WRF Model including PHS input (left) and from HRRR model not including PHS input (right) (Click to enlarge)

Severe weather on 15 April and 19 April affords another opportunity to compare Significant Tornado Parameter predictions from a 4-km WRF simulation that includes information from Polar Hyperspectral Soundings and a 3-km HRRR simulation that does not. Consider the example, above, at 2200 and 2300 UTC on 15 April and 0000 and 0100 UTC on 16 April. Each model shows the averages of 9 different forecasts valid at the given time. The HRRR output shows a maximum in STP over southeastern Missouri. The WRF output (driven by an initial field that includes information from Polar Hyperspectral Soundings) has a maximum in STP farther to the west where severe weather was occurring.

The example below is from the severe weather event on 19 April, again showing STP from the PHS-enhanced WRF simluation on the left and from the HRRR on the right. As on 15 April, the alignment of the severe weather events more closely matches the model predictions of STP when the model has PHS data are part of its input cycle.

Significant Tornado Parameter from WRF Model including PHS input (left) and from HRRR model not including PHS input (right) (Click to enlarge)

Imagery for this blog post is courtesy Qi Zhang, CIMSS; PHS model runs are available here in real time. This model output will be evaluated at the Hazardous Weather Testbed in May and June.


GOES-16 True Color imagery from the CSPP Geosphere site, 1701 – 1926 UTC on 19 April 2023

The animation above (created at the CSPP Geosphere site) shows abundant — but thin — clouds over the southern USA between 1700 and 1900 UTC on 19 April 2023, i.e., before the PHS imagery shown above from 0000 to 0300 UTC on 20 April. The satellite retrievals at 1700 UTC on the 19th, shown below, included both infrared and microwave components over Oklahoma, as shown below (image courtesy Bill Smith Sr). That infrared retrievals could occur is testimony to the broken cloud field. How do the satellite retrievals alter the relative humidities? That is also shown below for 1700 and 2300 UTC (top and bottom) for 850 and 700 mb (left and right). Satellite data has altered the RH such that deeper moisture is farther to the west.

1700 UTC Satellite Retrievals: Infrared (blue) or microwave (red) at 850 mb (top left) and 700 mb (top right). (Click to enlarge)
Relative Humidity differences in PHSnMWnABI fields at 850 and 700 mb (left and right, respectively) at 1700 and 2300 UTC on 19 April (top and bottom, respectively) (Click to enlarge); warmer colors depict more moisture.

The image below, created by Bill Smith Sr, shows how the changed low-level moisture field from the Polar Hyperspectral data in this case leads to a more accurate simulation (the SAT/WRF model on the right, compared to the HRR model) of where severe weather occurs.

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Severe Weather and Tornadoes over Oklahoma

Oklahoma City and surrounding areas were struck with severe weather last night, the night of Wednesday 2023-04-19. Nine tornadoes touched down in Oklahoma, making a total of 15 tornadoes from the storm system. Tornadoes were also reported in Nebraska and Iowa. This severe weather took the lives of three Oklahomans.... Read More

Oklahoma City and surrounding areas were struck with severe weather last night, the night of Wednesday 2023-04-19. Nine tornadoes touched down in Oklahoma, making a total of 15 tornadoes from the storm system. Tornadoes were also reported in Nebraska and Iowa. This severe weather took the lives of three Oklahomans.

Storm reports on 2023-04-19 from the Storm Prediction Center.

The radar reflectivity south of Norman, Oklahoma showed a distinct rotational pattern, which is noted in the animation below. The rotation can be seen in the reflectivity around the 00:20Z timestamp (7:20 pm local time).

Radar Reflectivity every five minutes over the Oklahoma City area from 2023-04-19 at 22:00Z to 2023-04-20 at 02:50Z. Rotation in the radar signature is annotated near the 00:20Z time stamp. You can recreate this animation in RealEarth.

The Storm Prediction Center, appropriately located in Norman, OK, has issued a slight categorical outlook for today, 2023-04-20.

The severe weather outlook for 2023-04-20.

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