This project is a continuation of previous studies, which employed an observation system simulation experiment (OSSE) framework based on the ensemble square-root filter (EnSRF) to evaluate the impact of data from multiple radar networks on thunderstorm forecasts. Simulated data from an existing WSR-88D radar and from a CASA (Collaborative Adaptive Sensing of the Atmosphere) radar network being installed in central Oklahoma were used.

The proposed advantages of the CASA radar network include a high-resolution sampling of the lower atmosphere using small, low-cost radars and the ability of the network of Doppler radars to dynamically adapt to changing conditions. These features should improve the detection of hazardous weather events in the lower atmosphere that may be missed by the existing WSR-88D network, and will provide more complete data for the initialization of numerical models.

Unlike our previous studies that were limited to comparing idealized supercell and multicell cases using five minute data assimilation cycles with full volume-scan data, additional sampling strategies and other factors will be considered and a new quasi-linear convective system example will be added. For each of these three storm types, the importance of temporal sampling, adaptive scanning strategies, forecast sensitivity, and an expanded radar network will be evaluated. Assimilations and forecasts are performed by incorporating the simulated CASA and WSR-88D radar data into the Advanced Regional Prediction System (ARPS) model using the EnSRF method.