8.2 The NOAA/CIMSS ProbSevere Model – Integration of NWP, Satellite, Lightning, and Radar data for Improved Severe Weather Warnings

Thursday, 14 January 2016: 8:45 AM
Room 225 ( New Orleans Ernest N. Morial Convention Center)
Michael J. Pavolonis, NOAA/NESDIS, Madison, WI; and J. L. Cintineo, J. Sieglaff, and D. T. Lindsey

In the era of “big data,” forecasters and decision makers need tools that can efficiently convert of large volumes of data into environmental intelligence for a variety of applications, including severe weather warning operations. In recognition of this need, NOAA's National Environmental Satellite, Data, and Information Service (NESDIS) Center for Satellite Applications and Research (STAR), in collaboration with the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies (CIMSS), developed a statistical model aimed at improving the timeliness and accuracy of severe weather warnings. The NOAA/CIMSS ProbSevere model utilizes Numerical Weather Prediction (NWP), geostationary satellite radiometer, lightning, and radar data to forecast the probability that a developing thunderstorm will first produce severe weather in the near-term (0-60 minutes). ProbSevere has been shown to add 10 minutes of lead-time to severe weather warnings 50% of the time. A significant portion of the lead-time can be attributed to the use of geostationary satellite data. Thus, the median lead-time is expected to increase with the next generation of Geostationary Operational Environmental Satellites (GOES-R). GOES-R will also provide lightning measurements that will help increase the skill of the ProbSevere model and possibly allow for more detailed predictions (e.g. probability of individual severe weather hazards). ProbSevere was successfully demonstrated at NOAA's Hazardous Weather Testbed (in 2014 and 2015) and is now available to several National Weather Service (NWS) Weather Forecast Offices. We will give an overview of the ProbSevere model, including performance statistics and user feedback, and describe how the model will benefit from the next generation of GOES satellites (GOES-R).
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