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
MONTHLY WEATHER REVIEW | AMER METEOROLOGICAL SOC | Published : 2013
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..View full abstract
Awarded by Office of Naval Research
Awarded by NOPP
Awarded by NRL
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.