Journal article

Comparison of Hybrid Ensemble/4DVar and 4DVar within the NAVDAS-AR Data Assimilation Framework

David D Kuhl, Thomas E Rosmond, Craig H Bishop, Justin McLay, Nancy L Baker



The effect on weather forecast performance of incorporating ensemble covariances into the initial covariance model of the four-dimensional variational data assimilation (4D-Var) Naval Research Laboratory Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) is investigated. This NAVDAS-AR-hybrid scheme linearly combines the staticNAVDAS-ARinitial background error covariance with a covariance derived from an 80-member flow-dependent ensemble. The ensemble members are generated using the ensemble transform technique with a (three-dimensional variational data assimilation) 3D-Var-based estimate of analysis error variance. The ensemble covariances are localized usi..

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University of Melbourne Researchers


Awarded by Office of Naval Research

Awarded by NOPP

Awarded by NRL

Funding Acknowledgements

We thank all those people responsible for the development of NAVDAS-AR, in particular, we thank the late Roger Daley who first formulated and initiated the development of NAVDAS-AR. We would also like to thank Liang Xu who was the PI of the project that ultimately led to the transition of NAVDAS-AR into operations. Boon Chua, Tim Hogan, Ben Ruston, James Goerss, and Pat Pauley also made major contributions to NAVDAS-AR. This research was started while D. D. Kuhl held a National Research Council Research postdoctoral fellowship at the Naval Research Laboratory, Washington, D. C., and continued during his NRL Jerome Karl Fellowship award. T. Rosmond acknowledges support from PMW-120 under Program Element 0603207N. C. H. Bishop acknowledges support from Office of Naval Research base funding via Program Element 0601153N, Task BE033-03-45, and NOPP funding via Program Element 0602435N. NAVDAS-AR was originally developed with ONR and PMW-120 funding under NRL base Program Elements 0601153N and 0602435N.