Ensemble Data Assimilation Applied to Hurricanes Katrina and Rita
Ryan Torn, NCAR/ASP
Most operational data assimilation systems employ special techniques, such as vortex bogusing or vortex repositioning, to assimilate observations near tropical cyclones (TC) because the employed quasi-fixed error statistics, which determine how to spread observations to model grid points, are not appropriate. The ensemble Kalman filter (EnKF) offers an attractive approach for TC state estimation because it assimilates observations using flow-dependent background error statistics based on short-term ensemble forecasts. This talk will describe the performance of a WRF EnKF system, which is cycled over the lifetime of Hurricanes Katrina and Rita (2005). While this EnKF system generally produces good track and intensity forecasts and has fewer spin-up problems, several challenges still exist for TC state estimation, especially at high resolution. In addition, the output from this EnKF system will be used to understand how initial condition errors can impact the predictability of these two storms.
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