Trends 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 severethunderstormoccurrences 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.
ASJC Scopus subject areas
- Atmospheric Science