A Comparison of Hybrid-Gain Versus Hybrid-Covariance Data Assimilation for Global NWP

Whitaker, J. S.; Shlyaeva, A.; Penny, S. G.

doi:10.1029/2022MS003036

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A Comparison of Hybrid-Gain Versus Hybrid-Covariance Data Assimilation for Global NWP

2022
By Whitaker, J. S. ; Shlyaeva, A. ; Penny, S. G.
Source: Journal of Advances in Modeling Earth Systems, 14, e2022MS003036

[PDF-1.42 MB]

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Journal Title:

Journal of Advances in Modeling Earth Systems
Personal Author:

Whitaker, J. S. ; Shlyaeva, A. ; Penny, S. G.

Whitaker, J. S. ; Shlyaeva, A. ; Penny, S. G. Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; PSL (Physical Sciences Laboratory) ; CIRES (Cooperative Institute for Research in Environmental Sciences)

OAR (Oceanic and Atmospheric Research) ; PSL (Physical Sciences Laboratory) ; CIRES (Cooperative Institute for Research in Environmental Sciences) Less -
Description:

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.
Keywords:

[+]

Data Assimilation Hybrid-covariance Hybrid-gain Kalman Filter (EnKF)
Source:

Journal of Advances in Modeling Earth Systems, 14, e2022MS003036
DOI:

https://doi.org/10.1029/2022MS003036
Format:

PDF
Document Type:

Journal Article
Funding:

Grant no. NA20OAR4600277
License:

https://creativecommons.org/licenses/by/4.0/
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CC BY
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Submitted
Main Document Checksum:

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urn:sha256:d874b4cee149bfb5843dea06bb74909d1bc4df607becffddb0211f31f93feb72
Download URL:

https://repository.library.noaa.gov/view/noaa/48994/noaa_48994_DS1.pdf
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A Comparison of Hybrid-Gain Versus Hybrid-Covariance Data Assimilation for Global NWP

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