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.

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!

The probability of “intense convection” using geostationary satellite data

September 27th, 2019 |

Researchers from NOAA and UW-CIMSS have developed an experimental model that predicts the “probability of intense convection” inferred from GOES ABI and GLM fields. The model is a convolutional neural network, which carries the assumption that the inputs are images and have spatial context. It is a great tool for image classification.

GOES-16 ABI CH02 reflectance (a visible channel), ABI CH13 brightness temperature (an infrared window channel [IR]), and GLM flash extent density (FED; generated using glmtools),  were used as inputs to the model. The model learned important features that have been traditionally difficult or expensive to code into an algorithm, such as pronounced overshooting tops (OTs), enhanced-V features, thermal couplets, above-anvil cirrus plumes (AACPs), strong brightness temperature gradients, cloud-top divergence, and texture from visible reflectance.

It is hoped that such a model may be able to one day:

  • provide earlier notice of developing or decaying intense convection
  • provide guidance in regions with no weather radars
  • provide a quantitative way to leverage 1-min mesoscale scans
  • ultimately improve the accuracy and lead time of severe weather warnings

The model is very experimental and is not yet running in real-time. The remainder of this post catalogues some examples of the deployed model on select scenes.

The movies below use as a background the CH02-CH13 “sandwich” product, whereby cloud-top 11-µm brightness temperature and 0.64-µm reflectance can be seen in tandem. This generally helps observers see how changes in storm-top structure correlate with changes in 11-µm brightness temperature. A grid of “probability of intense convection” was generated for each scene with a moving 32×32 pixel window (each pixel = ~2 km), with the model generating one probability for each window. These probabilities were then contoured with the 25%, 50%, and 90% contours as blue, cyan, and magenta. Preliminary severe local storm reports from the SPC rough log are also plotted as circles.

The example below shows that the model handled two separate severe wind threats in Missouri, identifying cold cloud top regions in the IR that also looked “bubbly” from the visible channel. As the sun was setting, a cold front lit up with very intense convection from Oklahoma through Missouri. Again, the model did a decent job highlighting the strongest areas of convection which correlated well with severe local storm reports. It should also be noted that the model does not seem to have significantly degraded output when the visible channel is missing (after sunset).


The next example is at a higher satellite viewing angle in western Nebraska, western South Dakota, and eastern Wyoming. The model again does a good job highlighting the strongest areas of storms. It should be noted that not every identified region has severe reports and not every severe report has a probability of intense convection ? 25%, but that there is generally good correspondence between reports and the model probabilities nonetheless.


This example is from the Southeast U.S. in more of a low-shear “microburst” environment instead of a high-shear “supercell” environment. You can see that instead of predicting high probabilities for all of the convective storms, the model exhibits the highest probabilities for the storm clusters that at least subjectively look the strongest.


This next example from the Central Plains demonstrates the ability to discern decaying convection, as the first storm moves into Missouri and then quickly diminishes in appearance and in probability. It also demonstrates the model’s ability to pick out multiple threat areas within a large cloud mass at night.


This is an example using mesoscale scans. Despite not being trained with 1-min data, the model predictions still look very fluid and reasonable. This could be an excellent way for scientists and forecasters to leverage 1-min observations in a quantitative manner.


Another example using GOES-East 1-min mesoscale scans. The model generally picks out the strongest portions of a MCS in Illinois and Indiana.


At a very high viewing angle, the model predicts probabilities of ?90% for a storm in Arizona. The storm did not generate severe reports, but was warned on by the NWS multiple times.


The model is deployed during an early autumn severe weather outbreak.

This example shows the model deployed on 1-min mesoscale scans over very intense thunderstorms in Argentina. It demonstrates that the model is generally applicable to anywhere in the world where advanced imager and GLM-like observations are present.


Here, a model was trained without GLM data and deployed for an example in the Alaska Panhandle, where GLM data is not available. This storm prompted a severe thunderstorm warning from the Juneau, AK NWS office. Note the change in the values of the probability contours. The maximum probability for this storm was 36% at 02:43 UTC. In a relative sense, the intense convection probability product could still be useful in unconventional regions, such as Alaska.