A "Dressed" Ensemble Kalman Filter Using the Hybrid Coordinate Ocean Model in the Pacific

作者：Wan Liying^*; Zhu Jiang; Wang Hui; Yan Changxiang; Bertino Laurent

来源：Advances in Atmospheric Sciences, 2009, 26(5): 1042-1052.

DOI：10.1007/s00376-009-7208-6

• Dressing Ensemble Kalman Filter (DrEnKF)
• HYbrid Coordinate Ocean Model
• root mean square errors

The computational cost required by the Ensemble Kalman Filter (EnKF) is much larger than that of sonic simpler assimilation schemes, such as Optimal Interpolation (OI) or three-dimension 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 sonic 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 four-year period. Ten dynamical ensemble seeds are each dressed by 10 static ensemble members selected from a 100-member 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 OT SST dataset. The results show that the DrEnKF and the 100-member EnKF yield similar root mean square errors (RMSE) at every model level. Error covariance matrices from the DrEnKF and the 100-member EnKF are also compared and show good agreement.

• 出版日期2009-9
• 单位国家海洋环境预报中心; 中国科学院大气物理研究所