Near Real Time Integration of Satellite and Radar Data for Probabilistic Nearcasting of Severe Weather

Thursday, 18 December 2014: 10:35 AM
Michael J Pavolonis1, John Cintineo2, Justin Sieglaff2 and Daniel T. Lindsey3, (1)NOAA Madison, NESDIS/STAR, Madison, WI, United States, (2)University of Wisconsin Madison, Cooperative Institute for Meteorological Satellite Studies, Madison, WI, United States, (3)NOAA Fort Collins, NESDIS/STAR, Fort Collins, CO, United States
The formation, maintenance, and severity of thunderstorms that produce large hail, strong winds, and tornadoes are often difficult to forecast due to their rapid evolution and complex interactions with environmental features that are challenging to directly observe. This paper describes an empirical, data driven, approach to forecasting severe convection through fusion of near real time data from several sources. More specifically, data from the Geostationary Operational Environmental Satellites (GOES), the Next Generation Weather Radar (NEXRAD) network, and the Rapid Refresh (RAP) numerical weather prediction (NWP) model are used to drive a naïve Bayesian classifier. Each observation source provides unique information during different periods of storm development (i.e., the pre-storm environment, storm initiation and growth, and hydrometeor intensification). The model is designed to provide warning guidance to forecasters in the near-term (0-60 min), by quantifying several key temporal and spatial attributes of developing convection. The probabilistic model, known as ProbSevere, has been running in near real time at the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) since April of 2013 and was formally evaluated by National Weather Service (NWS) forecasters at the Hazardous Weather Testbed (HWT) in the spring of 2014. Validation studies and forecaster feedback indicated that the ProbSevere model, which is driven by near real time data, could be used to improve severe weather warning operations. In this paper, we will give an overview of the ProbSevere model, including performance statistics, and describe how the model will benefit from the next generation of GOES satellites (GOES-R).