92nd American Meteorological Society Annual Meeting (January 22-26, 2012)

Wednesday, 25 January 2012: 10:30 AM
Development of the NCAR 4D-REKF Ensemble Data Assimilation and Forecasting System and a Comparison with Dart-EnKF and WRFVAR
Room 340 and 341 (New Orleans Convention Center )
Yubao Liu, NCAR, Boulder, CO; and L. Pan, Y. Wu, A. Bourgeois, J. Knievel, S. Swerdlin, X. Y. Huang, J. C. Pace, F. W. Gallagher, and S. F. Halvorson

A Four-Dimensional Relaxation Ensemble Kalman Filter (4D-REKF) modeling system for mesoscale analysis and forecasting has been developed jointly by NCAR and ATEC (Army 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). E-RTFDDA has been deployed for operational support at the U.S. Army Dugway Proving Ground since August 2007, and for wind energy prediction for Xcel Energy since May 2010. 4D-REKF is implemented by replacing the spatial weighting functions in the traditional Newtonian-relaxation station-nudging FDDA formulations with the Kalman gains computed with a local ensemble transform Kalman Filter (LETKF) scheme. 4D-REKF retains and leverages the advantages of both traditional Newtonian-relaxation and Ensemble Kalman Filter data assimilation schemes. It eliminates the ad-hoc specification of spatial weighting functions in the current station-nudging FDDA formulation. Furthermore, it extends the typical intermittent EnKF data assimilation method to a 4D continuous data assimilation scheme, which reduces the dynamic shocks often caused by the intermittent EnKF processes, alleviating the critical dependency on the background error covariance inflation, and augmenting an ability to assimilate all observations that may be available at irregular locations and times and an ability to assimilate the observations more effectively. The theoretical aspects, the key technical components, and the implementation challenges of the 4D-REKF system will be described. Preliminary test results with idealized modeling settings and with real-weather and real-data modeling will be presented. Furthermore, a comparison with the NCAR DART-EnKF and WRFVAR data assimilation system will also be discussed.

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