The Statistical Severe Convective Risk Assessment Model

John A. Hart NOAA/NWS/NCEP/Storm Prediction Center, Norman, Oklahoma

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Ariel E. Cohen NOAA/NWS/NCEP/Storm Prediction Center, Norman, Oklahoma

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Abstract

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 five parameters of SSCRAM is investigated in the present study. This system shows that severe storm probabilities do not vary strongly across the range of values for buoyancy parameters compared to vertical shear parameters. The significant tornado parameter [where “significant” refers to tornadoes producing (Fujita scale) F2/(enhanced Fujita scale) EF2 damage] exhibits considerable skill at identifying downstream tornado events, with higher conditional probabilities of occurrence at larger values, similar to effective storm-relative helicity, both findings being consistent with previous studies. Meanwhile, lifting condensation level heights are associated with conditional probabilities that vary little within an optimal range of values for tornado occurrence, yielding less skill in quantifying tornado potential using this parameter compared to effective storm-relative helicity. 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.

Corresponding author address: John Hart, Storm Prediction Center, 120 David L. Boren Blvd., Norman, OK 73072. E-mail: john.hart@noaa.gov

Abstract

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 five parameters of SSCRAM is investigated in the present study. This system shows that severe storm probabilities do not vary strongly across the range of values for buoyancy parameters compared to vertical shear parameters. The significant tornado parameter [where “significant” refers to tornadoes producing (Fujita scale) F2/(enhanced Fujita scale) EF2 damage] exhibits considerable skill at identifying downstream tornado events, with higher conditional probabilities of occurrence at larger values, similar to effective storm-relative helicity, both findings being consistent with previous studies. Meanwhile, lifting condensation level heights are associated with conditional probabilities that vary little within an optimal range of values for tornado occurrence, yielding less skill in quantifying tornado potential using this parameter compared to effective storm-relative helicity. 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.

Corresponding author address: John Hart, Storm Prediction Center, 120 David L. Boren Blvd., Norman, OK 73072. E-mail: john.hart@noaa.gov
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