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).