One of the major goals of the Collaborative and Adaptive Sensing of the Atmosphere (CASA) project is to utilize CASA radar data to improve short-term convective forecasts. The spatial and temporal fine-scale detail of CASA data makes it an excellent candidate for assimilation into numerical convective forecasts at a resolution sufficient to meaningfully predict tornadogenesis. One method that can be used to achieve this goal is data assimilation using an ensemble Kalman filter (EnKF), which is ideal because of the flow-dependent background error covariance it incorporates.

The Advanced Regional Prediction System (ARPS) is used to forecast a mesoscale convective vortex that passed through the CASA domain on May 9, 2007 and produced several tornadoes, including one with EF-1 intensity near Minco, OK. CASA and NEXRAD data, along with more traditional forms of data such as ASOS, RAOBs, profiler data, and Oklahoma Mesonet observations are assimilated. Two nested grids are used, one relatively coarse outer grid with 2 km horizontal grid spacing and a high-resolution inner grid with horizontal grid spacing of 100 m. Radar data are assimilated using the EnKF method on the inner grid, with all other data sources assimilated using the ARPS 3DVAR. Assimilation cycles with intervals of approximately 5 minutes are used by the EnKF.

To examine the benefit of CASA data, experiments are conducted on the high resolution grid that include radar data from NEXRAD alone, CASA alone, and both NEXRAD and CASA data. Also, the results of data assimilation using the EnKF method will be compared with those of similarly configured experiments using the 3DVAR method.