Analysis of WRF and MM5 mesoscale model forecasts to distinguish tornado outbreaks from primarily nontornadic severe weather outbreaks

This work is part of a project to assess the role of synoptic-scale processes in controlling the occurrence of tornado outbreaks. Determining the extent by which synoptic-scale processes are responsible for the occurrence of tornado outbreaks is of great importance for increased scientific understanding as well as for the improvement of severe weather forecasts. Furthermore, finding which synoptic-scale features at particular lead times are significant in the production of tornado outbreaks is a primary issue. Because even the most sophisticated operational numerical models do not explicitly forecast tornadoes, a method must be derived to distinguish between tornadic and primarily nontornadic severe weather outbreaks from model output. The use of meteorological covariates is necessary in order to determine if the model run is indeed distinguishing tornado outbreak cases from primarily nontornadic severe weather outbreaks. A major component of the project is to find useful covariates. To this end, NCEP/NCAR Reanalysis data are used to initialize and provide boundary conditions for mesoscale model runs of 10 tornado outbreaks and 10 primarily nontornadic severe weather outbreaks. Three runs of each case are generated: a 24-hr forecast, a 48-hr forecast, and a 72-hr forecast. Each run has four nested domains. The mother domain uses 150 km grid spacing. The innermost domain has 2 km grid spacing and is positioned in the same outbreak relative position for each case. The results of these simulations will be used to evaluate various candidate covariates to be used in later phases of the project.