9.2 The Importance of Satellite-based Predictors in the NOAA/CIMSS ProbSevere Model

Wednesday, 17 August 2016: 1:45 PM
Madison Ballroom CD (Monona Terrace Community and Convention Center)
Justin Sieglaff, CIMSS/Univ. of Wisconsin, Madison, WI; and M. J. Pavolonis and J. L. Cintineo

The NOAA/CIMSS ProbSevere model integrates derived data from geostationary satellite imagers, ground-based radar networks, continental and global numerical weather prediction models, and a total lightning detection network, to quantitatively produce a probability that any given storm in the continental U.S. will produce severe weather in the near-term (e.g., large hail, high wind, or tornado). The presentation will give an overview of the statistical model and its performance, including its automated object-centric approach that more accurately depicts temporal trends in satellite features. The presentation will focus on the impact of imager-derived data and ground-based total lighting data, the latter of which may demonstrate the expected benefit of space-borne total-lightning instruments (e.g., GOES-R Geostationary Lightning Mapper). Future work regarding additional satellite imager and total lightning predictors, and the potential for an OCONUS ProbSevere model will also be discussed.
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