11.1 Hybrid variational/ensemble Kalman filter data assimilation with HWRF

Thursday, 10 January 2013: 8:30 AM
Room 9C (Austin Convention Center)
Jeffrey S. Whitaker, NOAA/Earth System Research Laboratory, Boulder, CO; and H. Winterbottom and M. Tong

A global hybrid 3D-variational/Ensemble Kalman filter (3DVAR/ENKF) data assimilation system has been implemented at NCEP for global weather prediction. The system uses an 80-member ensemble to help estimate the flow-dependent background-error covariance used for the 3D-Var system. Significant improvements in global forecast skill, particularly for tropical cyclones have been realized using the new system. We have tested a regional version of this system for hurricane prediction using the Hurricane Weather Research and Forecasting (HWRF) model in near-real time during the 2012 Atlantic hurricane season. Forecasts initialized with this hybrid system, using either the global (i.e., GFS) ensemble or a HWRF ensemble to estimate the background-error covariance, are compared to HWRF forecasts initialized directly from the GFS/ENKF control analysis.
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