Assimilation of simulated CASA radar data and prediction of varied convective storm types using ensemble square-root Kalman Filter

This project is a continuation of previous studies, which employed an observation system simulation experiment (OSSE) framework based on the ensemble square-root Kalman filter (EnKF) 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 are 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 a single idealized supercell case, additional types of convective storms, including multi-cellular and quasi-linear convective systems, are simulated within the radar network. Assimilations and forecasts are performed by incorporating the simulated radar data into the Advanced Regional Prediction System (ARPS) model using the EnKF method. For each of these storm types, the importance of temporal sampling, forecast sensitivity, and areal coverage of the radars will be evaluated.