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.
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.
Severe weather season is underway across the southern U.S. NOAA and CIMSS are using satellite, radar, and lightning observations of thunderstorms to develop and evaluate artificial intelligence (AI) tools that forecast and diagnose convection.The NOAA/CIMSS ProbSevere portfolio has several such tools to help forecasters keep tabs on storms.
LightningCast
ProbSevere LightningCast is a model that uses images of geostationary ABI data from GOES-16 or GOES-17 to predict where lightning will strike (as observed by the GLM) out to 60 minutes. We’ve found that the product frequently provides 15-30 minutes of lead-time to lightning initiation, measured from the 30-40% probability range (the most skillful range). LightningCast will be evaluated at the 2022 NOAA Hazardous Weather Testbed and at certain offices within the NWS. LightningCast may be able to one day aid forecasters in providing decision support services and general convective initiation situational awareness. Below is a movie of LightningCast output (contours) overlaid on the GOES-16 daytime cloud phase distinction RGB and GLM flash-extent density, for the developing severe storms in Texas on March 21st.
IntenseStormNet
Another image-based AI model within ProbSevere is called IntenseStormNet, which seeks to identify the most intense regions of thunderstorms. It uses images of ABI and GLM data as predictors to probabilistically diagnose storm intensity from a geostationary perspective. Its goal is to identify intense parts of storms as humans do: holistically; by picking up on the spatial and multispectral features that imagery captures, such as overshooting tops, cold-U/AACP, cloud-top texture patterns, and stark cloud edges. In a paper published in 2020, we found that high probabilities from IntenseStormNet are frequently correlated with severe weather reports. Below shows IntenseStormNet output (contours) for some of the storms over Texas on March 21st, most of which spawned hail, damaging winds, and several tornadoes. IntenseStormNet contours can also be tracked over time, providing a novel way to investigate convective properties of storms.
ProbSevere v3
IntenseStormNet output is also being used as a predictor within the experimental ProbSevere v3, which uses satellite, radar, lightning, and NWP data, and machine-learning (ML) models to forecast the probabilities of hail, severe winds, and tornadoes in the next 60 minutes. While ProbSevere v2 is operational at NOAA’s Centers for Environmental Prediction, ProbSevere v3 is being evaluated by NWS forecasters at the 2022 Hazardous Weather Testbed. An analysis of thousands of storms from 2021 showed that additional predictors and the more sophisticated ML models in v3 improve upon v2 performance. ProbSevere uses multi-sensor storm tracking and feature extraction to predict probabilities of severe weather across the U.S. Below are several animations of ProbSevere v3 output in Texas on March 21st.
ProbSevere v3 output (contours), MRMS composite reflectivity, and NWS severe weather warnings for storms in northern Texas on March 21st.ProbSevere v3 output (contours), MRMS composite reflectivity, and NWS severe weather warnings for storms in central Texas on March 21st.
