A comparison of hybrid-gain versus hybrid-covariance data assimilation for global NWP

Two methods for incorporating a time-invariant, high-rank covariance estimate in an ensemble-based data assimilation system for global weather prediction are compared. The hybrid-covariance approach linearly combines the static and ensemble-based covariance estimate in a four-dimensional variational solver, whereas the hybrid-gain approach blends analysis increments computed separately using a three-dimensional variational solution and an ensemble Kalman filter solution. Results show that the simpler and less expensive hybrid-gain approach performs similarly if the incremental normal-mode balance constraint applied to the ensemble-part of the hybrid-covariance update is turned off.

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Copyright 2022 American Geophysical Union.


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Author Whitaker, Jeffrey S.
Shlyaeva, Anna
Penny, Stephen G.
Publisher UCAR/NCAR - Library
Publication Date 2022-08-04T00:00:00
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Topic Category geoscientificInformation
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Metadata Date 2023-08-18T18:18:10.605454
Metadata Record Identifier edu.ucar.opensky::articles:25606
Metadata Language eng; USA
Suggested Citation Whitaker, Jeffrey S., Shlyaeva, Anna, Penny, Stephen G.. (2022). A comparison of hybrid-gain versus hybrid-covariance data assimilation for global NWP. UCAR/NCAR - Library. http://n2t.net/ark:/85065/d7rb78c9. Accessed 08 February 2025.

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