The Geospatial and Temporal Distributions of Severe Thunderstorms from High-Resolution Dynamical Downscaling

Abstract

rends in severe thunderstorms and the associated phenomena of tornadoes, hail, and damaging winds have been difficult to determine because of the many uncertainties in the historical eyewitness-report-based record. The authors demonstrate how a synthetic record that is based on high-resolution numerical modeling may be immune to these uncertainties. Specifically, a synthetic record is produced through dynamical downscaling of global reanalysis data over the period of 1990?2009 for the months of April?June using the Weather Research and Forecasting model. An artificial neural network (ANN) is trained and then utilized to identify occurrences of severe thunderstorms in the model output. The model-downscaled precipitation was determined to have a high degree of correlation with precipitation observations. However, the model significantly overpredicted the amount of rainfall in many locations. The downscaling methodology and ANN generated a realistic temporal evolution of the geospatial severe-thunderstorm activity, with a geographical shift of the activity to the north and east as the warm season progresses. Regional time series of modeled severe-thunderstorm occurrences showed no significant trends over the 20-yr period of consideration, in contrast to trends seen in the observational record. Consistently, no significant trend was found over the same 20-yr period in the environmental conditions that support the development of severe thunderstorms.