Impact of an Ensemble of Ocean Data Assimilations in ECMWF's next generation ocean reanalysis system
Marcin Chrust, Anthony T. Weaver, Philip Browne, Hao Zuo, Magdalena, Alonso Balmaseda

TL;DR
This paper introduces an ensemble data assimilation approach for ECMWF's next-generation ocean reanalysis system, improving background-error covariance estimation by blending climatological and flow-dependent information, leading to reduced errors.
Contribution
It develops a hybrid covariance modeling method using ensemble-based flow-dependent and climatological estimates for ECMWF's ocean reanalysis system.
Findings
Significant reduction in background errors with the new hybrid covariance model.
Demonstrated improved analysis quality during the Argo-rich period.
Enhanced specification of flow-dependent vertical and horizontal correlation scales.
Abstract
An Ensemble of Data Assimilations (EDA) can provide valuable information on the analysis and short-range forecast uncertainties. The present ECMWF operational ocean analysis and reanalysis system, called ORAS5, produces an ensemble but does not exploit it for the specification of the background-error covariance matrix , a key component of the data assimilation system. In this article, we describe EDA developments for the ocean, which take advantage of the short-range forecast ensemble for specifying, in two distinct ways, parameters of a covariance model representation of . First, we generate a climatological ensemble over an extended period to produce seasonally varying climatological estimates of background-error variances and horizontal correlation length-scales. Second, on each assimilation cycle, we diagnose flow-dependent variances from the ensemble and…
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Taxonomy
TopicsAtmospheric and Environmental Gas Dynamics
