Tangent-linear and ensemble-based four-dimensional data assimilation strategies applied for assimilating conventional data and field observations for Hurricane Karl (2010)

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Thursday, 8 January 2015: 11:30 AM
131AB (Phoenix Convention Center - West and North Buildings)
Jonathan Poterjoy, Pennsylvania State University, University Park, PA; and F. Zhang

Two advanced four-dimensional ensemble data assimilation systems are applied for studying the genesis of Hurricane Karl (2010) using conventional observations and measurements collected during the Pre-Depression Investigation of Cloud Systems in the Tropics (PREDICT) field campaign. Both methods combine strategies from four-dimensional variational (4DVar) and Ensemble Kalman filter (EnKF) data assimilation techniques that have been developed for the Weather Research and Forecasting model. The first method, denoted E4DVar, operates in a manner similar to the traditional 4DVar data assimilation system, but with hybrid climate/ensemble background errors. The second method, denoted 4DEnVar, uses an ensemble of nonlinear model trajectories to replace the function of tangent linear and adjoint model operators in 4DVar, thus improving the parallelization of the data assimilation. Simulations initialized from E4DVar and 4DEnVar analyses provide track, genesis and intensity forecasts for Karl that are more accurate than an ensemble hybrid data assimilation method based on 3DVar (E3DVar). The two 4-D data assimilation methods are applied for studying Karl's genesis, while comparing their theoretical advantages and disadvantages for an application where the system dynamics evolve quickly in time, and are constrained by an unusually high number of in situ observations.