Authors: Liying Wan Jiang Zhu Hui Wang Changxiang Yan Laurent Bertino
Publish Date: 2009/09/19
Volume: 26, Issue: 5, Pages: 1042-1052
Abstract
The computational cost required by the Ensemble Kalman Filter EnKF is much larger than that of some simpler assimilation schemes such as Optimal Interpolation OI or threedimension variational assimilation 3DVAR Ensemble optimal interpolation EnOI a crudely simplified implementation of EnKF is sometimes used as a substitute in some oceanic applications and requires much less computational time than EnKF In this paper to compromise between computational cost and dynamic covariance we use the idea of “dressing” a small size dynamical ensemble with a larger number of static ensembles in order to form an approximate dynamic covariance The term “dressing” means that a dynamical ensemble seed from model runs is perturbed by adding the anomalies of some static ensembles This dressing EnKF DrEnKF for short scheme is tested in assimilation of real altimetry data in the Pacific using the HYbrid Coordinate Ocean Model HYCOM over a fouryear period Ten dynamical ensemble seeds are each dressed by 10 static ensemble members selected from a 100member static ensemble Results are compared to two EnKF assimilation runs that use 10 and 100 dynamical ensemble members Both temperature and salinity fields from the DrEnKF and the EnKF are compared to observations from Argo floats and an OI SST dataset The results show that the DrEnKF and the 100member EnKF yield similar root mean square errors RMSE at every model level Error covariance matrices from the DrEnKF and the 100member EnKF are also compared and show good agreement
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