Journal article

A Nonvariational Consistent Hybrid Ensemble Filter

Craig H Bishop, Bo Huang, Xuguang Wang



A consistent hybrid ensemble filter (CHEF) for using hybrid forecast error covariance matrices that linearly combine aspects of both climatological and flow-dependent matrices within a nonvariational ensemble data assimilation scheme is described. The CHEF accommodates the ensemble data assimilation enhancements of (i) model space ensemble covariance localization for satellite data assimilation and (ii) Hodyss's method for improving accuracy using ensemble skewness. Like the local ensemble transform Kalman filter (LETKF), the CHEF is computationally scalable because it updates local patches of the atmosphere independently of others. Like the sequential ensemble Kalman filter (EnKF), it seria..

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University of Melbourne Researchers


Awarded by Office of Naval Research

Awarded by NASA NIP

Awarded by NOAA

Funding Acknowledgements

This work was conceived while a guest of Juan Antonio Rengel Ortega and the Instituto Hidrografico de la Marina (IHM), Armada Espanola, and Cadiz-Espana. Thanks to Juan and IHM for their hospitality. Craig Bishop acknowledges financial support from the Department of Navy's Engineers and Scientists Exchange Program and the Office of Naval Research Grant N0001413WX00008. Bo Huang and Xuguang Wang acknowledge support from NASA NIP Grant NNX10AQ78G and NOAA Grant NA14NWS4680021.