Convective Meteorology (Mesoscale Dynamics)

The Statistical Severe Convective Risk Assessment Model (SSCRAM)

Ariel Cohen

Ph.D., NOAA/NWS/NCEP/Storm Prediction Center, Norman, Oklahoma, University of Oklahoma School of Meteorology Instructor

21 October 2016, 3:00 PM

National Weather Center, Room 5600
120 David L. Boren Blvd.
University of Oklahoma
Norman, OK

This study introduces a system that objectively assesses severe thunderstorm nowcast probabilities based on hourly mesoscale data across the contiguous United States during the period from 2006 to 2014. Previous studies have evaluated the diagnostic utility of parameters in characterizing severe thunderstorm environments. In contrast, the present study merges cloud-to-ground lightning flash data with both severe thunderstorm report and Storm Prediction Center Mesoscale Analysis system data to create lightning-conditioned prognostic probabilities for numerous parameters, thus incorporating null-severe cases. The resulting dataset and corresponding probabilities are called the “Statistical Severe Convective Risk Assessment Model” (SSCRAM), which incorporates a sample size of over 3.8 million 40-km grid boxes. A subset of parameters of SSCRAM is investigated in the present study. The systematic assessment of probabilities using convective environmental information could have applications in present-day operational forecasting duties and the upcoming warn-on-forecast initiatives. Real-time examples of SSCRAM's utility in severe thunderstorm forecasting and an evaluation of SSCRAM output will be presented.

Additionally, SSCRAM is used to assess the utility of severe-thunderstorm parameters commonly used by forecasters in anticipating thunderstorms that produce significant tornadoes (i.e., causing F2/EF2 or greater damage) from June through October. The utility during June through October is compared to that during other months. Previous studies have identified some aspects of the summertime challenge in severe storm forecasting, and this study provides an in-depth quantification of within-year variability of significant tornado predictability. Conditional probabilities of significant tornadoes downstream of lightning occurrence using common parameter values, such as effective-layer significant tornado parameter, convective available potential energy, and vertical shear, are found to substantially decrease in the months of June through October compared to other months. Furthermore, conditional probabilities of significant tornadoes during June through October associated with these parameters are nearly invariable regardless of value, highlighting the challenge of using objective environmental data to attempt to forecast significant tornadoes from June through October.

Convective Meteorology (Mesoscale Dynamics) Seminar Series website