This year will mark the 50th anniversary of the “Super Outbreak” of April 1974. The event was unique for the number of tornado touchdowns, the number of F5 tornadoes, and the conditions that fostered their formation. Also unique is that the University of Wisconsin-Madison Space Science and Engineering Center recently digitized over 66,000 Applications Technology Satellite (ATS)-1 and -3 images from the late 1960s to early 1970s. These include ATS-3 visible images from April 3, 1974 that clearly show the development of the squall lines. ATS were experimental NASA geostationary satellites that carried the Spin Scan Cloud Camera. The camera, developed at SSEC, allowed for nearly continuous viewing of weather systems, like the Super Outbreak.

The 3-4 April 1974 Super Outbreak (NWS Wilmington OH | Wikipedia | Interacive WebMap | Monthly Weather Review) was one of the largest and most deadly tornado outbreaks on record in the United States. Several images from the ATS-3 satellite are shown below (thanks to the work of SSEC Satelite Data Services and Atmospheric, Oceanic and Space Sciences Library staff!). Large clusters of thunderstorms that produced the tornadoes are very apparent, along with a hazy plume of blowing dust that moved across much of North Texas.

HT

This blog post leverages a similar post from 2023.

View only this post Read Less

With a strong midlatitude cyclone centered over Iowa on 31 March 2023, the Storm Prediction Center (SPC) outlined 2 areas of relatively rare High Risk for severe weather — and GOES-16 (GOES-East) Total Precipitable Water along with the Lifted Index (LI) and Convective Available Potential Energy (CAPE) Derived Stability Indices (above) showed that a corridor of moisture and instability was in place along and ahead of the primary cold front (surface analyses).

1-minute Mesoscale Domain Sector GOES-16 “Clean” Infrared Window (10.3 µm) images (below) included plots of time-matched (+/- 3 minutes) SPC Storm Reports during the period from 1615 UTC on 31 March to 1137 UTC on 01 April.

One event of note was the EF3-rated tornado that affected Little Rock, Arkansas — GOES-16 “Red” Visible (0.64 µm) and “Clean” Infrared Window (10.3 µm) images (below) included time-matched SPC Storm Reports.

1-minute GOES-16 Visible/Infrared Sandwich RGB images (below) included polygons of Severe Thunderstorm and Tornado Warnings — note that at one point a bold Tornado Emergency was issued.

Farther to the north, 1-minute GOES-16 Visible images (below) showed the widespread severe weather across the Midwest during the daytime hours.

1-minute GOES-16 Visible/Infrared Sandwich RGB images (below) included plots of time-matched Local Storm Reports — showing the storm which produced an EF4-rated tornado that moved from Wapello into Johnson County in eastern Iowa (NWS Quad Cities summary).

View only this post Read Less

The NOAA/CIMSS ProbSevere LightningCast model uses GOES-R ABI reflectances and brightness temperatures along with artificial intelligence methods to predict next-hour lightning anywhere in the GOES-R field of view. It is particularly adept at nowcasting lightning initiation.

A long, potent cold front spawned a number of storms from Iowa to Texas today (many of which were severe). The GOES-16 mesoscale sectors were providing rapid-scan service to forecasters in the middle of country, providing 1-minute updates of satellite imagery.

In Texas, the cold front is clearly delineated between clear sky to its west and low clouds to its east, in the warm/moist sector. The probabilities of lighting from LightningCast increased rather quickly from <10% to >75% in many regions, prior to the first detection of GLM flashes (Figure 1). Elevated probabilities of lightning were observed from 15 to 35 minutes prior to lightning initiation for most of these storm cells.

Further north, along the Kansas / Missouri border, LightningCast accurately highlighted the line of lightning initiation forced by the cold front (Figure 2). Along this part of the front, there is good evidence that the model’s contours of probability are leading the convective regions, anticipating the prevailing motion. While LightningCast currently only uses a single time of data to make predictions, it seems to have learned (to some extent) the motion of storms from patterns in single images of reflectance and brightness temperature data.

LightningCast will be evaluated by forecasters at the 2023 Hazardous Weather Testbed. Forecaster feedback is important to help direct research-and-development and potential transition-to-operations efforts.

View only this post Read Less

The animation above, created at the CSPP Geosphere site (direct link to the animation), shows showers dissipating over and south of the Samoan island chain shortly after sunset, and then redeveloping to the north of the island chain. The redevelopment in the RGB imagery followed the usual color change associated with developing convection: faint pink/red colors that became deeper and deeper red as clouds grew. Note that the RGB also has characteristic color — blue — that suggests clear skies. This means that clear-sky Level 2 products from GOES-18 such as Total Precipitable Water (TPW) and Derived Stability Indices might give useful information to help a forecaster anticipate the redevelopment of convection. The animations below show those TPW and Lifted Index overlain on top of GOES-18 “Clean Window” Band 13 infrared (10.3) imagery, from 0600 – 1000 UTC; the animations end before the developing convection generated clouds to mask the view. Note also that the Default AWIPS enhancements for TPW and Lifted Index have been altered, as noted in the caption.

The convection at the beginning of the animation was aligned along the southern edge of abundant total precipitable water, and it dissipated as it moved to slightly more stable air, that is, air with slightly less negative Lifted Indices (brighter yellow in the animation). There is greater instability to the north — and that’s where convection subsequently developed as noted in the Night MIcrophysics RGB animation.

Make sure you look at Level 2 Products such as TPW and Lifted Index (or CAPE, or K Index, or Total Totals Index) when skies are clear to get a better idea of gradients in regions that are void of data other than satellite information.

View only this post Read Less