| A ＂Dressed＂ Ensemble Kalman Filter Using the Hybrid Coordinate Ocean Model in the Pacific |
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| 引用本文： | 万莉颖,朱江,王辉,闫长香.A ＂Dressed＂ Ensemble Kalman Filter Using the Hybrid Coordinate Ocean Model in the Pacific[J].大气科学进展,2009,26(5):1042-1052. |
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| 作者姓名： | 万莉颖 朱江 王辉 闫长香 |
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| 作者单位： | National Marine Environmental Forecasting Center, Beijing 100086,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,National Marine Environmental Forecasting Center, Beijing 100086,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Mohn-Sverdrup Center/Nansen Environmental and Remote Sensing Center, Bergen N-5006, Norway |
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| 基金项目： | The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan) |
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| 摘 要： |
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| 关 键 词： | Dressing Ensemble Kalman Filter （DrEnKF） HYbrid Coordinate Ocean Model root mean square errors |
| 收稿时间： | 4/4/2008 12:00:00 AM |
A “dressed” Ensemble Kalman Filter using the Hybrid Coordinate Ocean Model in the Pacific |
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| Liying?Wan,Jiang?Zhu,Hui?Wang,Changxiang?Yan,Laurent?Bertino.A “dressed” Ensemble Kalman Filter using the Hybrid Coordinate Ocean Model in the Pacific[J].Advances in Atmospheric Sciences,2009,26(5):1042-1052. |
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| Authors: | Liying Wan Jiang Zhu Hui Wang Changxiang Yan Laurent Bertino |
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| Institution: | National Marine Environmental Forecasting Center, Beijing 100086,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,National Marine Environmental Forecasting Center, Beijing 100086,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Mohn-Sverdrup Center/Nansen Environmental and Remote Sensing Center, Bergen N-5006, Norway |
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| 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 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 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 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 OI 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. |
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| Keywords: | Dressing Ensemble Kalman Filter (DrEnKF) HYbrid Coordinate Ocean Model root mean square errors |
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