Evaluation of enhancements to the Rapid Refresh GSI 3DVAR-ensemble hybrid data assimilation system

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Wednesday, 5 February 2014: 9:30 AM
Room C202 (The Georgia World Congress Center )
Ming Hu, NOAA Earth System Research Laboratory, Boulder, CO; and D. C. Dowell, S. S. Weygandt, S. G. Benjamin, J. S. Whitaker, and C. Alexander

NOAA's Rapid Refresh system (RAP) is an hourly-updated regional data-assimilation and forecasting system that uses the Weather Research and Forecasting (WRF-ARW) model with 13-km horizontal grid spacing and the Gridpoint Statistical Interpolation (GSI) analysis package. The RAP version 1 has been running operationally at NCEP since May 2012. Since then, many new advanced features have been developed and applied in a real-time experimental RAP version 2 (RAPv2) system. Among the RAPv2 enhancements, switching to a 3DVAR-Ensemble hybrid assimilation procedure within GSI is the most important analysis upgrade. Application of the hybrid technique in the RAPv2 resulted in an immediate significant positive impact, as was reported in 93th AMS Annual Meeting, in January 2013.

The current RAPv2 GSI hybrid data analysis system is using coarser resolution GEFS ensemble forecasts at 6-hour intervals to contribute half of the background error covariance. The hybrid configuration is based on default values, and the configuration could potentially be improved by tuning certain parameters, such as the relative weights of the ensemble-based and 3DVar covariances. The initial application of GSI hybrid within the RAPv2 has already significantly improved middle and upper level wind and moisture forecasts. In 2013, we tested a number of enhancements to the GSI hybrid configuration based on the needs of RAP, including using GEFS ensemble forecasts at one or three hour intervals, tuning the ratio of static and ensemble BE, allowing the BE ratio to vary vertically, and finding better vertical and horizontal localizations for this mesoscale application. We also tested using coarser ensemble forecasts in the GSI hybrid system to speed up the analysis for real-time application. In the long-term, we plan to test a GSI-based RAP ensemble, which would be initialized from GEFS ensemble members and then cycled hourly with an ensemble Kalman filter, toward a goal of improving RAP forecasts of near surface fields and localized weather phenomena.

This talk will describe results from these tests and report on the progress of the GSI hybrid application for the RAP system.