An Assessment of Areal Coverage of Severe Weather Parameters for Severe Weather Outbreak Diagnosis

Abstract

he areal extent of severe weather parameters favorable for significant severe weather is evaluated as a means of identifying major severe weather outbreaks. The first areal coverage method uses kernel density estimation (KDE) to identify severe weather outbreak locations. A selected severe weather parameter value is computed at each grid point within the region identified by KDE. The average, median, or sum value is used to diagnose the event?s severity. The second areal coverage method finds the largest contiguous region where a severe weather parameter exceeds a specified threshold that intersects the KDE region. The severe weather parameter values at grid points within the parameter exceedance region are computed, with the average, median, or sum value used to diagnose the event?s severity. A total of 4057 severe weather outbreaks from 1979 to 2008 are analyzed. An event is considered a major outbreak if it exceeds a selected ranking index score (developed in previous work), and is a minor event otherwise. The areal coverage method is also compared to Storm Prediction Center (SPC) day-1 convective outlooks from 2003 to 2008. Comparisons of the SPC forecasts and areal coverage diagnoses indicate the areal coverage methods have similar skill to SPC convective outlooks in discriminating major and minor severe weather outbreaks. Despite a seemingly large sample size, the rare-events nature of the dataset leads to sample size sensitivities. Nevertheless, the findings of this study suggest that areal coverage should be tested in a forecasting environment as a means of providing guidance in future outbreak scenarios.