4.2 Validation of the NCAR 4DREKF Data Assimilation Scheme with OSSE-based Numerical Experiments

Wednesday, 26 January 2011: 8:45 AM
2B (Washington State Convention Center)
Linlin Pan, NCAR, Boulder, CO; and Y. Liu, Y. Wu, T. Warner, and J. C. Pace

A 4DREKF (Four-Dimensional Relaxation Ensemble Kalman Filter) mesoscale data assimilation scheme is developed at NCAR with the Weather Research and Forecasting Version 3.2 model, (Liu et. al. 2010; this conference). To validate this data assimilation approach for mesoscale data assimilation and prediction, observation simulation and data assimilation experiments based on the observing system simulation experiments (OSSEs) approach are conducted. A high-resolution WRF model with a refined physics suite is employed to generate a virtual nature. An interpolation module is used to extract “observations” from the output of the WRF natural run according to the locations and times of real and hypothetical observation platforms. The simulated observations are applied in data assimilation and forecast experiments with the 4DREKF systems. The modeling experiments include a) “Perfect-Model-Perfect-Data” experiments; b) “Perfect Model-Imperfect-Data” experiments; and c) data types and data density impact experiments. Comparison experiments of 4DREKF analysis and forecasts with those of cold-start runs, obs-nudging WRF-RTFDDA runs, and NCAR DART-EnKF (Data Assimilation Research Testbed – Ensemble Kalman Filter) runs were also conducted. The experiment results indicate that the 4DREKF modeling system presents apparent advantages over the other approaches tested.
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