Observation Error Modeling and EnKF OSSEs Examining the Impact of Spatial and Temporal Resolutions and Errors of Phased-Array Radar
Yasuko Umemoto, University of Oklahoma, Norman, OK; and T. Lei, T. Y. Yu, and M. Xue
The S-band Phased Array Radar (PAR) at the National Weather Radar Testbed (NWRT) in Norman, Oklahoma can adaptively scan multiple regions of interest and provide high-quality, rapidly updated weather observations by electronically beam steering. Among efforts to better realize its potential for improving convective-storm analysis and prediction, an ensemble Kalman filter (EnKF) system developed for the Advanced Regional Prediction System (ARPS) has recently been enhanced to assimilate radar data radial by radial, with proper beam pattern and range weighting functions in all three directions. This capability allows us to take advantage of the azimuthal and elevation over-sampling and beam multiplexing capabilities provided through the PAR.
In this study, our earlier Observing System Simulation Experiments (OSSEs) assimilating over-sampled data using EnKF are extended to take into account of spatially inhomogeneous and scan-interval-dependent observation error estimates. Through proper modeling of the expected error in the observations using different scanning strategies, the results of the OSSEs become more realistic, and an optimal combination of the spatial and temporal resolutions and data precision is sought through the EnKF OSSEs. The special and scan interval-dependent observation errors are estimated using parameters produced from simulated radar waveforms. Real over-sampled data sets have been collected during the spring of 2008 using NWRT PAR and efforts will be made to assimilate the data and examine their effectiveness in initialization convective storm forecast.
Extended Abstract (220K)
Poster Session 1, Data Assimilation and Impact Studies
Monday, 12 January 2009, 2:30 PM-4:00 PM, Hall 5
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