9.1 The Impact of Total Lightning Information in the NOAA/CIMSS ProbSevere Model

Wednesday, 9 November 2016: 9:00 AM
Pavilion Ballroom (Hilton Portland )
John L. Cintineo, CIMSS/Univ. of Wisconsin, Madison, WI; and M. J. Pavolonis, J. Sieglaff, and J. C. Brunner

The NOAA/CIMSS ProbSevere model is a statistical model that processes and employs geostationary satellite imagery, merged radar, short-term numerical weather prediction, and ground-based total lightning data to predict the probability that any given thunderstorm may produce severe weather in the near-term. ProbSevere utilizes a naïve Bayesian classifier with an ‘ingredients-based’ approach to predictor selection. ProbSevere incorporated total lightning data prior to the 2016 convective season, and is currently being evaluated in a real-time framework by more than 50 National Weather Service (NWS) offices. This talk will demonstrate the benefits of total lightning flash rate data on the statistical model performance, as well as the pitfalls. Results from the Hazardous Weather Testbed and the NWS evaluations, as well as an offline statistical analysis of lightning impact will also be presented.
- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner