Study on the Optimal Scanning Strategies of Phase-Array Radar through Ensemble Kalman Filter Assimilation of Simulated Data

The phased-array radar (PAR) of the National Weather Radar Testbed (NWRT) in Norman, Oklahoma represents a paradigm shift for weather radar observations. It can adaptively scan multiple regions of interest and provide rapidly-updated weather observations. Through beam multiplexing, increased measurement accuracy can be achieved without the increase of scan time. Further, by partitioning the array elements into sub-panels to form spaced antennas, cross-beam winds can be measured, though longer dwell time and typically a lower accuracy also than the radial velocity measurements.

Recently, the ARPS (Advanced Regional Prediction System) ensemble Kalman filter (EnKF) data assimilation system is upgraded to handle more flexible forms of radar scans, and to be able to assimilate data of individual radials. An existing sophisticated radar emulator is enhanced to allow for additional scanning modes possible with PAR. Spatially varying estimates of observation error variances will also be produced by the radar emulator and used by the EnKF assimilation system. Simulated data collected on high-resolution numerical simulations of convective storms using various scanning strategies will be assimilated into the ARPS model using the EnKF system, and the impact of the data and the optimality of the scanning modes will be analyzed. The effects of the proper modeling of the observation errors will also be examined. All these will be studied in the context of both perfect and imperfect assimilation and prediction model. In the latter case, methods to alleviate the model error will be exploited.