Validation and Evaluation of the NCAR 4D-REKF ensemble data assimilation and forecasting system

A Four-Dimensional Relaxation Ensemble Kalman Filter (4D-REKF) mesoscale analysis and forecasting system has been developed jointly by NCAR and ATEC (Amy Test and Evaluation Command). 4D-REKF is built upon the multi-model (MM5 and WRF), multi-approach (perturbations), and multi-scale (nested-grid) E-RTFDDA (Ensemble Real-Time Four-Dimensional Data Assimilation and forecasting system) that has been running operationally at the Army Dugway Proving Ground. E-RTFDDA model members employ Newtonian-relaxation 4D data assimilation algorithms to achieve rapid cycling of continuous 4D analysis and forecasting. 4D-REKF makes use of the multi-model E-RTFDDA forecasts to compute the background error covariance and construct the spatial weight functions for each observation (i.e. Kalman gain). The Kalman gains are then ingested into E-RTFDDA models to replace the idealized distance-dependent observation weighting functions in the original nudging model. A Local Ensemble Kalman Filter (LEKF) approach is employed to take account of multi-observations. 4D-REKF retains and leverages the advantages of both traditional Newtonian-relaxation and Ensemble Kalman Filter data assimilation schemes. It eliminates the shortcoming of empirical specification of spatial weight functions in the current station-nudging FDDA formulation. On the other hand, it extends the traditional (intermittent) EnKF data assimilation method to a 4D continuous data assimilation paradigm that greatly mitigates the dynamic shocks associated with the intermittent EnKF processes. Furthermore, 4D-REKF also greatly reduces the critical dependency on the background error covariance inflation with the traditional EnKF and permits effective assimilation of all observations that may be available at irregular locations and times. In this paper, we will describe the science and initial implementation of 4D-REKF, present the validation experiments and comparison studies with NCAR DART-ENKF, WRF-3DVAR, WRF-4DVAR and transitional Newtonian nudging schemes. Statistics of the model output from real-time parallel data assimilation and forecasting experiments at Dugway Proving Ground will be also discussed.