Warn-on-Forecast (WoF) for Flash Flood Producing Extreme Convective Rainfall

Accurate short-term (0-6 h) forecasts of hazardous convective weather (i.e. heavy rainfall, tornadoes, large hail and damaging winds) are crucial to reduce loss of life, property and disruption from high impact events. Currently, hydrological models use rainfall observations as the forcing mechanism but much longer lead times could be provided if short-term extreme convective rainfall forecasts were sufficiently accurate. As part of NOAA’s OWAQ Joint Technology Transfer Initiative, this study further investigates how to improve the data assimilation techniques to produce accurate model initial conditions in order to improve short-term forecasts of severe thunderstorms with a focus on flash flood producing, convectively driven extreme rainfall events. A frequent-update-cycle convective-scale ensemble data assimilation and forecast system is developed using the WRF-ARW model and NOAA GSI-EnKF data assimilation software. The system assimilates NWS conventional observations, WSR-88D reflectivity, radial velocity and geostationary satellite cloud water path retrievals. The capability to assimilate high-temporal frequency ground based remote sensing planetary boundary layer observations (i.e. AERI and Doppler Lidar) into the GSI-EnKF system are currently underway. Retrospective 0-6 h probabilistic ensemble forecasts from several recent record-breaking heavy rainfall and flash flood events will be generated. To evaluate how the frequent-update-cycle convective-scale ensemble data assimilation and forecast system performs in predicting these events, we will use both qualitative and quantitative verification metrics.