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| 污染模型中参数订正的集合Kalman滤波扩展状态变量法 | |
| 引用本文： | 吴祝慧,韩月琪,王成林,黄娟.污染模型中参数订正的集合Kalman滤波扩展状态变量法[J].环境监控与预警,2012,4(3):36-56. |
| 作者姓名： | 吴祝慧 韩月琪 王成林 黄娟 |
| 作者单位： | 1. 金陵科技学院基础部,江苏 南京,211169 2. 解放军理工大学气象学院,江苏南京,211101 3. 江苏省环境监测中心,江苏南京,210036 |
| 基金项目： | 国家自然科学基金项目（40805046）；江苏省自然科学基金项目（BK2010128)；公益性行业（气象）专项课题（GYHY(QX)2007-06-15，GYHY200906009） |
| 摘 要： | 污染模型中不确定参数的精确订正对于提高模型的精度有着重要的意义。在集合Kalman滤波（EnKF）同化方法的基础上，提出了对模型中不确定参数进行订正的EnKF扩展状态变量法，将不确定参数看成和模型状态变量一样的量，根据观测资料对不确定变量进行订正，以达到订正参数的目的。采用一个简化的空气质量方程，对模型参数订正方案进行检验，结果证明，提出的方案可行和有效。同时发现，随着观测资料精度的提高，无论是参数还是模型的状态变量，估计分析值的精度也得到相应的提高。 |
| 关 键 词： | 污染模型 参数订正 集合Kalman滤波 扩展状态变量法 |
| 收稿时间： | 2010/12/1 0:00:00 |
| 修稿时间： | 2010/12/18 0:00:00 |
The?Research?of?Extended?State?Variable?Method?in?Ensemble Kalman?Filter?for?Estimating?Uncertain?Parameters?in?Pollution?Model |
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| WU Zhu-hui , HAN Yue-qi , WANG Cheng-lin , HUANG Juan.The?Research?of?Extended?State?Variable?Method?in?Ensemble Kalman?Filter?for?Estimating?Uncertain?Parameters?in?Pollution?Model[J].Environmental Monitoring and Forewarning,2012,4(3):36-56. | |
| Authors: | WU Zhu-hui HAN Yue-qi WANG Cheng-lin HUANG Juan |
| Institution: | 1.Basis Department,Jinling Institute of Technology,Nanjing,Jiangsu 211169,China;2.Institute of Meteorology,PLA University of Science and Technology,Nanjing,Jiangsu 211101,China;3.Jiangsu Provincial Environmental Monitoring Center,Nanjing,Jiangsu 210036,China) |
| Abstract: | The exact estimation of uncertain parameters in pollution model makes great sense in enhance the precision of numerical model.The method of extended state variable based on Ensemble Kalman Filter(EnKF) is introduced to estimate the uncertain parameters.That is the uncertain parameters as the model state variables that can be corrected by observational data.A simple quality equation of air is used to test the method of correcting model parameters.The results of experiment show that the method is feasible and effective.At the same time,the precision of estimated value of parameters and state variables is improved with the elevation of the observational data’s precision. |
| Keywords: | pollution?model ?Parameter?estimation ?Ensemble?Kalman?Filter ?Extended?state?variable?method |
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