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| 变分同化方法反演海气耦合模型参数的研究 | |
| 引用本文： | 杜华栋,黄思训,蔡其发,程亮.变分同化方法反演海气耦合模型参数的研究[J].海洋通报(英文版),2009,11(2):13-22. |
| 作者姓名： | 杜华栋 黄思训 蔡其发 程亮 |
| 作者单位： | 1. 中国人民解放军理工大学气象学院,江苏,南京,211101 2. 南京信息工程大学,江苏省气象灾害重点实验室,江苏,南京,210044 3. 北京2433信箱,北京,100081 |
| 基金项目： | The study was supported by the National Science Foundation of China （40775023） and the Science Foundation for Doctor of the Institute of Meteorology of PLA University of Sci. and Tech. |
| 摘 要： | 采用变分资料同化技术，结合最优控制思想，对一个海气耦合模型的模式参数和强迫项进行了反演，结果表明，采用该方法对模式进行优化，既可以补偿模式参数不准确性给预报带来的误差，又可以对模式参数本身进行修正和估计，为将来在实际应用中改善更复杂的预报模式、提高预报准确率提供了一个可借鉴的思路。 |
| 关 键 词： | 变分资料同化 海气耦合模型 最优控制 |
Studies of Variational Assimilation for the Inversion of the Coupled Air-sea Model |
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| DU Hua-dong,HUANG Si-xun,CAI Qi-fa,CHENG Liang.Studies of Variational Assimilation for the Inversion of the Coupled Air-sea Model[J].Marina Science Bulletin,2009,11(2):13-22. | |
| Authors: | DU Hua-dong HUANG Si-xun CAI Qi-fa CHENG Liang |
| Institution: | 1. The Institute of Meteorology of the PLA University of Science and Technology, Nanjing 211101, Jiangsu Province China; 2. Jiangsu Key Laboratory of Meteorology Disaster, NUIST Nanjing 210044, Jiangsu Province, China 3. Beijing PO# Box 2433, Beijing 100081, China) |
| Abstract: | For the prediction of ENSO, the accuracy of the model including the parameters, initial value and others of the model is important, which can be retrieved by the variational data assimilation methods developed in recent years. However, when the nonlinearity of the model is quite strong, the effect of the improvement made by the 4-D variational data assimilation may be poor due to the bad approximation of the tangent linear model to the original model. So in the paper the ideas in the optimal control is introduced to improve the effect of 4-DVAR in the inversion of the parameters of a nonlinear dynamic ENSO model. The results indicate that when the terminal controlling term is added to the cost functional of 4DVAR, which originated from the optimal control, the effect of the inversion may be largely improved comparing to the traditional 4DVAR, as can be especially obvious from the phase orbit of the model variables. The results in the paper also suggest that the method of 4DVAR in combination with optimal control cannot only reduce the error resulting from the inaccuracy of the model parameters but also can correct the parameters itself. This gives a good method in modifying the model and improving the quality of prediction of ENSO. |
| Keywords: | variational data assimilation coupled air-sea mode optimal control |
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