Forecasting thunderstorm characterisitics that have a high impact on air traffic flow

The short-term (0-8 hr) prediction of thunderstorms and their characteristics (organization, severity, orientation (for squall lines), storm spacing) is vital air traffic flow management. Nowcasting systems based on radar observations have skill at predicting storm characteristics in the 0-2 hour time frame; however, this skill rapidly declines owing to the difficulties of nowcasting storm initiation and evolution. High-resolution convection-permitting NWP runs, which no longer parameterize the convection, are able to resolve storm structures and organization; however, the skill of NWP (when comparing pixel-by-pixel at fine scales) remains rather low owing to intensity and phase (space and time) errors. Note that assimilation of radar data improves the model forecasts at the short leadtimes, however, at this time data assimilation cannot beat extrapolation forecasts at leadtime of less than 2 hours. While the skill of NWP in predicting the actual location of storms is poor, the high resolution runs have apparent skill in predicting the timing, evolution and organization of storms. The goal of this study is to determine whether high resolution NWP has skill at predicting the statistical characteristics of storms relevant to air traffic flow management at leadtimes > 3 hours (e.g., relevant for strategic planning). Data from the Weather Research and Forecasting – Advanced Weather Research (WRF-ARW) model run in realtime twice per day (00 and 12 UTC) at 4 km resolution on the NCAR supercomputer are used to evaluate the capability of convection-permitting simulations to produce realistic storm structures and their evolution. In this study we focus on the model's ability to simulate the range of characteristics of the storms occurring in the SouthEastern US in July of 2006. The TITAN (Thunderstorm Identification Tracking and Nowcasting) software has been extended to determine storm characteristics (e.g., storm coverages, size distribution, orientation, spacing, interconnectedness, etc) in the model and the observations (using the WSR-88D national mosaic provided by WSI).