J16.2
Tests of a hybrid variational-ensemble data global assimilation system for hurricane prediction

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Thursday, 27 January 2011: 11:15 AM
Tests of a hybrid variational-ensemble data global assimilation system for hurricane prediction
2B (Washington State Convention Center)
Jeffrey Whitaker, NOAA/ESRL, Boulder, CO; and D. T. Kleist, X. Wang, and T. Hamill

Tests of a hybrid data assimilation system based on the operational Gridpoint Statistical Interpolation (GSI) variational system and an experimental ensemble Kalman filter (EnKF) with the NCEP Global Forecast System (GFS) model are presented for the 2010 hurricane season. The flow-dependent background error covariances provided by the EnKF, which is run at T254 (~ 60 km) resolution, are used to update a single T574 (~27 km) GFS first-guess forecast. The flow-dependence provided by the EnKF covariances are found to significantly alter the T574 analysis, producing smaller and more intense tropical cyclones compared to the operational GSI 3DVar analysis. We will examine the impact of the ensemble-based background-error covariances in the hybrid system on the ensuing tropical cycle track and intensity forecasts. The impact of recentering the EnKF analysis ensemble around the high-resolution hybrid analysis will be assessed.