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).

Using GOES ABI and deep learning to nowcast lightning

September 2nd, 2020 |

NOAA and CIMSS are developing a product that uses a deep-learning model to recognize complex patterns in weather satellite imagery to predict the probability of lightning in the short term. Deep learning is a branch of machine learning based on artificial neural networks, which have the ability to automatically learn targeted features in the data by approximating how humans learn.

A convolutional neural network (CNN) was trained on over 23,000 images of GOES-16 Advanced Baseline Imager (ABI) data to predict the probability of lightning, within any given ABI pixel, in the next 60 minutes. The CNN was trained using 118 days of data collected between May and August of 2018. Images of GOES Geostationary Lightning Mapper (GLM) flash-extent density (created with glmtools) were used as the source of lightning observations. Note that GLM is an optical sensor that observes both in-cloud and cloud-to-ground lightning.

The CNN currently uses four ABI channels: band 2 (0.64-µm), band 5 (1.6-µm), band 13 (10.3-µm), and band 15 (12.3-µm). Bands 2 and 5 are only utilized under sunlit conditions. Utilization of additional channels and time sequences of images is under investigation. The model uses a semantic image segmentation architecture to assign the probability of lighting in the next 60 minutes to each pixel in the image. The model is very computationally efficient, only needing 30 seconds to process the ABI CONUS domain and 3 seconds to process an ABI mesoscale domain using multithreading on a 40-CPU linux server.

Currently, the model only utilizes satellite radiances. Thus, it can be applied to nearly any spatial domain covered by the ABI or an ABI-like sensor (e.g. AHI). Based on near-real-time testing, the model routinely nowcasts lightning initiation with 10-30 minutes of lead-time. We expect the skill and lead-time will increase as new predictors (e.g. more ABI fields, NWP, radar where available) are added to the model.

Below are a sampling of recent examples. The base images are 0.64-µm reflectance, with GLM-derived flash-extent density overlaid as filled semi-transparent polygons. The flash-extent density is the accumulated number of flashes within the previous 5 minutes. The CNN-derived probabilities are displayed as contours at select levels (near-real-time output is available through RealEarth).

The overall objective is to improve lightning nowcasts in support of aviation, mariners, and outdoor events/activities. Beyond improving the CNN, our work will focus on packaging the output into actionable information for forecasters and other decision makers.

A cold front in Iowa


Thunderstorm development on sea-breeze boundaries in Florida and the Bahamas


Diurnally and orographically forced storms in the Southwest U.S. and Rocky Mountains


Storms in central Oklahoma, on the edge of Hurricane Laura’s cloud shield


A couple of examples over the Northeast U.S.


A boundary of convection on the southern bank of Lake Ontario


Storms in a warm sector in IL/IN/OH, perhaps along an outflow boundary


Southeast U.S. offshore region

The background image in this example transitions from 10.3-µm brightness temperature to 0.64-µm reflectance, while the flash-extent density enhancement also changes, in an attempt to enhance contrast.

Supercells in the Southeast

May 6th, 2020 |

A cold front with ample moisture and instability ahead of it spawned numerous strong storms in the Southeast U.S. yesterday; particularly one long-lived supercell in South Carolina. A convolutional neural network model (CNN) was deployed in realtime on the 1-min GOES-16 mesoscale sector imagery. The model produces an “Intense Convection Probability” (ICP). The inputs for the model are the GOES-16 ABI 0.64 µm reflectance, 10.3 µm brightness temperature, and GLM flash extent density. It was trained to identify “intense” convection as humans do, associating features with intense convection such as strong overshooting tops, thermal couplets (“cold-U/V”), above anvil cirrus plumes (AACP), and strong cores of total lightning.

The animation below shows the ICP contours overlaid ABI 0.64 µm + 10.3 µm sandwich imagery, annotated with preliminary severe storm reports.

The long-lived supercell in South Carolina exhibited AACP and cold-U features, and produced numerous severe wind and hail reports (up to the size of tennis balls). While the NOAA/CIMSS ProbSevere models handled this storm well, the ICP ramped up on a couple of severe storms in northern Georgia before ProbSevere did. ICP for these cells exceeded 90% 15-18 min before ProbWind reached 50%. The ICP may be able to provide additional lead time and confidence to ProbSevere guidance for certain storms, utilizing spectral and electrical information from geostationary satellites. Incorporating ICP into ProbSevere is an active area of current research.

ProbSevere storm contours and MRMS MergedReflectivity for storms in GA and SC. The main or “inner” ProbSevere contour is shaded by the probability of any severe weather, while the outer contour is shaded by the probability of tornado, which appeared when that value was at least 3%, in this example.

An accumulation of ProbSevere storm centroids (white to pink squares, 50% --> 100%), NWS severe weather warnings, and SPC severe local storm reports from 12Z on May 5th to 12Z on May 6th [click to enlarge]

An accumulation of ProbSevere storm centroids (white to pink squares, 50% –> 100%), NWS severe weather warnings, and SPC severe local storm reports from 12Z on May 5th to 12Z on May 6th [click to enlarge]