Severe thunderstorms in Michigan produce a fatal EF-3 tornado in Gaylord

May 20th, 2022 |

GOES-16 “Red” Visible (0.64 µm) and “Clean” Infrared Window (10.35 µm) images, with time-matched Local Storm Reports plotted in blue [click to play animated GIF | MP4]

1-minute Mesoscale Domain Sector GOES-16 (GOES-East) “Red” Visible (0.64 µm) and “Clean” Infrared Window (10.35 µm) images (above) showed thunderstorms that moved across the northern portion of Lower Michigan on 20 May 2022. These storms produced hail (up to 3.0 inches in diameter), damaging winds (as high as 76 mph) and an EF-3 tornado that struck Gaylord (SPC Storm Reports | NWS Gaylord summary). Note that METAR reports were not available at Gaylord (and also about 30 miles to the west-southwest, at Bellaire) after the time of the tornado and damaging wind reports, due to widespread power outages (which affected about 1/3 of customers in Ostego County). 

A 2-panel comparison of GOES-16 Visible and Infrared images — which includes time-matched plots of SPC Storm Reports — is shown below.

GOES-16 “Red” Visible (0.64 µm, top) and “Clean” Infrared Window (10.35 µm, bottom) images, with time-matched SPC Storm Reports plotted in red/cyan [click to play animated GIF | MP4]

Pulsing overshooting tops exhibited cloud-top infrared brightness temperatures as cold as -79oC — which represented an Equilibrium Level (EL) overshoot of 1 to 1.5 km, according to a special Gaylord rawinsonde launched at 19 UTC (below).

Plot of 19 UTC rawinsonde data at Gaylord, Michigan [click to enlarge]

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GOES-16 “Red” Visible (0.64 µm) and “Clean” Infrared Window (10.35 µm) images at 1948 UTC, with the initial tornado report location plotted in blue [click to enlarge|

A toggle between GOES-16 Visible and Infrared images at 1948 UTC (above) includes the initial tornado report location plotted in blue. Note the offset between the overshooting top and the tornado report — this is due to parallax (below).

GOES-16 parallax correction direction (green) and magnitude (in km, red) [click to enlarge]

As the thunderstorms initially began moving inland from Lake Michigan and producing damaging winds near the northwest coast of Lower Michigan, a toggle between Suomi-NPP VIIRS True Color RGB and Infrared Window (11.45 µm) images (below) revealed overshooting tops with infrared brightness temperatures as cold as -87.7oC (darker purple enhancement).

Suomi-NPP VIIRS True Color RGB and Infrared Window (11.45 µm) images, with Local Storm Reports plotted in blue [click to enlarge]

GOES World

May 18th, 2022 |
GOES-17, -18, -16 (West-to-Central-to-East) CIMSS Natural Color imagery at local noon, 15 May 2022. GOES-18 is Preliminary/Non-Operational (click to enlarge)

The image above (credit to Rick Kohrs from SSEC/CIMSS) shows Advanced Baseline Imager (ABI) data from GOES-17 (West), GOES-18 (Central, Preliminary/Non-Operational), and GOES-16 (East) on 15 May 2022. This “Local Noon CIMSS Natural Color” image is created by blending vertical strips of true-color imagery at local noon, starting in the east and proceeding westward. This was a rare opportunity for the GOES-R Series as GOES-18 was only at the central location (89.5W) for a limited time. A larger (5509×4207) version of this image is also available.

Other CIMSS Blog entries have introduced GOES-18, the latest in the GOES-R series. NOAA and NASA recently released the first ABI (Advanced Baseline Imager) imagery from GOES-18 (including this 2-min video). GOES-T was launched on 1 March 2022. Currently GOES-18 is “drifting” out west to be near the “West” position. GOES-18 is slated to become NOAA’s operational GOES-West in early 2023 (GOES-18 Post Launch Test and Transition Plan) after a thorough post-launch test period.

SSEC/CIMSS scientists (notably Rick Kohrs) create daily imagery that blends vertical strips of true-color imagery at local Noon, starting near the dateline and proceeding westward. Recent images are available at this website and include data from 5 geostationary satellites: Himawari, GOES-West, GOES-East, Meteosat-Prime, and Meteosat-IODC. There are multiple other blog posts featuring and explaining the local-noon composite.

Bolt out of the blue

May 10th, 2022 |

Florida is one of the lightning capitals of the world, so residents need to be constantly aware of lightning safety. NOAA/CIMSS LightningCast might be able to help with that.

A tree and home in Sebring, Florida were suddenly struck by lightning on the morning of Saturday, May 7th. A line of storms was edging its way eastward. A neighbor who was outside at the time said, “It wasn’t raining. It was nice and warm. It was cloudy, but that was it. And then boom!” This underscores how easy it is to be caught unaware of potential lightning danger.

LightningCast can help with users’ situational awareness. LightningCast is an experimental deep-learning model trained on thousands of GOES-R ABI and GLM images to predict the probability of next-hour lightning occurrence. In the animation below, the red dot is Sebring, Florida.

LightningCast probability contours, GOES-16 ABI imagery (grayscale background), and GOES-16 GLM flash-extent density (shaded color). The red dot is the approximate location of the “bolt out of the blue”.
Florida homeowner stunned by nearby lightning strike. Credit: FOX13 Tampa Bay

Below is a time series of LightningCast probability and GLM observations around the home in Sebring. Lightning struck the tree and home at 8:21 EDT, marked by the vertical black line below. You can see a rapid increase in probability of lightning from 11:26 to 11:36 UTC (7:26 to 7:36 EDT), reaching 70%. This was about 25 minutes before the first nearby lightning strike and 45 minutes before the Sebring home was struck.

The animation below from the National Weather Service lightning safety page shows that most lightning casualties occur before a thunderstorm is fully overhead, or before it fully departs the area, when people might not realize their vulnerability to lightning and don’t seek shelter soon enough or leave shelter too soon.

Animation depicting the threat of lightning casualties as a function of a hypothetical storm moving into the area.

LightningCast in Tampa, Florida

April 8th, 2022 |

Thunderstorms were slowly but surely edging their way dangerously close to Raymond James Stadium in Tampa, FL, on April 2nd. The New York Yankees and Atlanta Braves had just completed a spring training game at the stadium, when two people were struck by lightning in one of the parking lots surrounding the stadium (they were hospitalized but reported to be in stable condition).

ProbSevere LightningCast is an experimental deep-learning model that is running in near-real time at CIMSS. It uses images of GOES-R Advanced Baseline Imager (ABI) visible, near-IR, and longwave-IR channels to predict the probability of lightning (as observed by the GOES-R Geostationary Lightning Mapper [GLM]) in the next 60 minutes.

Below is a time series of the LightningCast probability and GLM-observed lightning at and near Raymond James Stadium (left panel), along with an animation of LightningCast probability contours, GOES-16 0.64-µm reflectance (from a 1-minute mesoscale sector), and GLM flash-extent density (right panel) near the stadium (red circle). In this way, users can see how the model’s probabilities evolved over time at a specific location and within the vicinity.

Figure 1: Time series of LightingCast probability and GLM-observed lightning at Raymond James Stadium (left). Animation of LightningCast contours, GOES-16 visible reflectance, and GLM flash-extent density (right) near the stadium (red circle).

Police officers responded to the two individuals struck by lightning at 3:45 PM local time (19:45 UTC). Based on the footprint of the GLM flash-extent density, they were struck at approximately 3:32 PM. The LightningCast probability of lightning was 75% 30 minutes before the lightning strike (remaining mostly above 50% between 3:00 and 3:32 PM). The probability of lightning first reached 50% about 1 hour before the lightning strike, and lightning started occurring within the vicinity (within 25 km) about 45 minutes before the strike.

Output from LightningCast, which leverages the high spatial, temporal, and spectral information found in GOES-R ABI, can help objectively quantify the short-term threat of convective hazards such as lightning. The model could perhaps be used by forecasters to advise outdoor venues such as stadiums to take mitigating actions sooner, or by individuals to help make safe decisions.

Figure 2: Annotated time series of LightningCast probability of lightning and GLM observations near Raymond James Stadium.