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.

Forecasting lightning

July 15th, 2021 |

Lightning safety is important for aircraft, mariners, and many outdoor activities. CIMSS is working to evaluate a model that nowcasts lightning. This model was trained using GOES-16 ABI visible, near-infrared, and long-wave infrared channels, as well as GOES-16 Geostationary Lightning Mapper (GLM) observations. It predicts the probability of lightning (IC or CG, as observed by GLM) in the next 60 minutes at any given point. The model routinely provides lead-time to lightning initiation of 20 minutes or more. We’re hopeful that one day such a model will help forecasters provide guidance for aviators, mariners, and decision support services (DSS) for things like sporting events, festivals, and theme parks. Near-real-time model output can be viewed using SSEC’s RealEarth.

Below are a few examples, with the forecast lightning probability contoured over the daytime cloud phase RGB and GOES-16 GLM flash-extent density.

So this summer, whether you’re going to the South Carolina beach,

or sailing in the Gulf of Maine,

or hiking in the Rocky Mountains,

or catching the first MLB game in Iowa,

be on the lookout for lightning!

ProbSevere products over the Southern Plains

May 3rd, 2021 |

The NOAA/CIMSS ProbSevere portfolio contains AI models for nowcasting convective weather. I’ll use Monday’s severe weather over the Southern Plains to highlight several of them.

A strong cold front spawned numerous severe-hail, wind, and tornado producing storms over Texas and Oklahoma, aided by very large values of convective available potential energy (CAPE; > 4000 J/kg).  You can see numerous storm reports in Figure 1.

210503_rpts Reports Graphic

Storm Prediction Center’s preliminary severe storm reports for May 3rd, 2021.

Probsevere version 2 (PSv2) is an operational set of models at NOAA, which predict the probability of severe hail, severe wind, and tornadoes, in the next 60 minutes. The models are storm-centric, and the models’ domain is the entire contiguous United States (CONUS).  These models use MRMS (radar), GOES (satellite), short-term NWP, and terrestrial-based lightning observations to generate probabilistic guidance of severe hazards. Figure 2 shows output from an experimental version (PSv3), which includes additional MRMS, GOES, and NWP fields as predictors in a machine learning model.

Figure 2: ProbSevere v3 contours (colored, around storms), MRMS MergedReflectivity, and NWS severe weather warnings (yellow and red boxes) for storms over the Southern Plains. The second outer contour around some storms is colored by the probability of tornado.

 

Another ProbSevere product is a convolutional neural network that uses GOES-R ABI and GLM images to detect regions of intense convection, and is often correlated with strong overshooting tops, “bubbly-like” texture in visible imagery, strong lightning cores, and the cold-U/above-anvil cirrus plume signature. The intense convection probability (ICP) can be run on the 1-minute mesoscale scans as well as 5-minute CONUS sector scans aboard the GOES satellites. The ICP does not require radar data, and may also be able to operate on data from satellites with similar intruments (e.g., Meteosat Third Generation). ICP output is being used as a predictor in the experimental ProbSevere v3.

 

Predicting when and where lightning will occur is also important for many users, such as mariners, aviators, and outdoor event managers. The probability of lightning model (PLTG) is also a convolutional neural network, using images of visible, near-infrared, and longwave-infrared channels to nowcast lightning occurrence in the next 60 minutes. The purple-to-orange shaded regions in the video below show GLM flash-extent density (i.e., flashes passing through a location).