3A.5
NOAA/CIMSS Prob Severe Model: Integrating Remotely Sensed Observations of Deep Convection

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Monday, 3 November 2014: 2:30 PM
Madison Ballroom (Madison Concourse Hotel)
Justin Sieglaff, CIMSS/Univ. of Wisconsin, Madison, WI; and M. J. Pavolonis, J. L. Cintineo, D. T. Lindsey, and E. Loken

The NOAA/CIMSS ProbSevere model is a statistical model that integrates satellite-derived, radar-derived, and NWP-derived data to forecast the probability that a developing thunderstorm will first produce severe weather in the near-term. National Weather Service (NWS) forecasters evaluated the ProbSevere model during the Experimental Warning Program at the Hazardous Weather Testbed (HWT) during the spring of 2014, as well as at the Omaha, NE, and Milwaukee, WI NWS weather forecast offices, as part of the GOES-R Proving Ground (PG) experiment during the spring and summer of 2014. The statistical model (a na´ve Bayesian framework) employs multi-scale, multi-sensor object identification and tracking, and uses temporal trends in satellite fields, radar intensity metrics, and NWP environmental parameters as predictors. After a brief overview of the fused product, user feedback accumulated from forecaster surveys, blogposts, and discussions at the HWT and GOES-R PG will be presented. Product strengths, limitations, and future research areas will be summarized.