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.
document
https://n2t.org/ark:/85065/d7rb78c9
eng
geoscientificInformation
Text
publication
2016-01-01T00:00:00Z
publication
2022-08-04T00:00:00Z
Copyright 2022 American Geophysical Union.
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