Development and verification of a new wind speed forecasting system using an ensemble Kalman filter data assimilation technique in a fully coupled hydrologic and atmospheric model

Numerous studies have shown that soil moisture distribution and land surface vegetative processes profoundly influence atmospheric boundary layer development and weather processes on local and regional scales. We have used the PF.WRF model to explore the connections between the land surface and the atmosphere in terms of land surface energy flux partitioning and coupled variable fields including hydraulic conductivity, soil moisture, and wind speed and demonstrated that reductions in uncertainty in these coupled fields realized through assimilation of soil moisture observations propagate through the hydrologic and atmospheric system.

The sensitivities found in this study will enable further studies to optimize observation strategies to maximize the utility of the PF.WRF-DART forecasting system.