| ZHAO Jian,CHEN Xueen,XU Jiangling,HU Wei,CHEN Jinrui,Pohlmann Thomas.Assimilation of surface currents into a regionalmodel over Qingdao coastal waters of China[J].Acta Oceanologica Sinica,2013,32(7):21-28 |
| Assimilation of surface currents into a regionalmodel over Qingdao coastal waters of China |
| Assimilation of surface currents into a regionalmodel over Qingdao coastal waters of China |
| 投稿时间：2010-07-24 修订日期：2012-05-08 |
| DOI：10.1007/s13131-013-0328-y |
| 中文关键词: Qingdao coastal waters surface currents Ensemble Kalman filter Finite Volume Coastal Ocean Model (FVCOM) |
| 英文关键词:Qingdao coastal waters surface currents Ensemble Kalman filter Finite Volume Coastal Ocean Model (FVCOM) |
| 基金项目:The National High Technology Research and Development Program of China under contract No. 2007AA09Z117; the Science and Technology Project of the North China Sea Brach of SOA under contract No. 2012A01; the Joint BMBF-WTZ Project of China under contract No. CHN 09/031. |
| 作者 | 单位 | E-mail | | ZHAO Jian | College of Physical and EnvironmentalOceanography, Ocean University of China, Qingdao 266100, China | | | CHEN Xueen | College of Physical and EnvironmentalOceanography, Ocean University of China, Qingdao 266100, China | xchen@ouc.edu.cn | | XU Jiangling | North China Sea Brach, State Oceanic Administration, Qingdao 266003, China | | | HU Wei | North China Sea Brach, State Oceanic Administration, Qingdao 266003, China | | | CHEN Jinrui | College of Physical and EnvironmentalOceanography, Ocean University of China, Qingdao 266100, China | | | Pohlmann Thomas | University of Hamburg, Hamburg D-20146, Germany | |
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| 中文摘要: |
| Surface currentsmeasured by high frequency (HF) radar arrays are assimilated into a regional oceanmodel over Qingdao coastal waters based on Kalman filter method. A series of numerical experiments are performed to evaluate the performance of the data assimilation schemes. In order to optimize the analysis procedure in the traditional ensemble Kalman filter (ENKF), a different analysis scheme called quasiensemble Kaman filter (QENKF) is proposed. The comparisons between the ENKF and the QENKF suggest that both them can improve the simulated error and the spatial structure. The estimations of the background error covariance (BEC) are also assessed by comparing three different methods: Monte Carlo method; Canadian quick covariance (CQC) method and data uncertainty engine (DUE) method. A significant reduction of the root-mean-square (RMS) errors between model results and the observations shows that the CQC method is able to better reproduce the error statistics for this coastal ocean model and the corresponding external forcing. In addition, the sensibility of the data assimilation system to the ensemble size is also analyzed by means of different scales of the ensemble size used in the experiments. It is found that given the balance of the computational cost and the forecasting accuracy, the ensemble size of 50 will be an appropriate choice in the Qingdao coastal waters. |
| 英文摘要: |
| Surface currentsmeasured by high frequency (HF) radar arrays are assimilated into a regional oceanmodel over Qingdao coastal waters based on Kalman filter method. A series of numerical experiments are performed to evaluate the performance of the data assimilation schemes. In order to optimize the analysis procedure in the traditional ensemble Kalman filter (ENKF), a different analysis scheme called quasiensemble Kaman filter (QENKF) is proposed. The comparisons between the ENKF and the QENKF suggest that both them can improve the simulated error and the spatial structure. The estimations of the background error covariance (BEC) are also assessed by comparing three different methods: Monte Carlo method; Canadian quick covariance (CQC) method and data uncertainty engine (DUE) method. A significant reduction of the root-mean-square (RMS) errors between model results and the observations shows that the CQC method is able to better reproduce the error statistics for this coastal ocean model and the corresponding external forcing. In addition, the sensibility of the data assimilation system to the ensemble size is also analyzed by means of different scales of the ensemble size used in the experiments. It is found that given the balance of the computational cost and the forecasting accuracy, the ensemble size of 50 will be an appropriate choice in the Qingdao coastal waters. |
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