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大气科学  2006 

集合卡尔曼平滑和集合卡尔曼滤波在污染源反演中的应用

DOI: 10.3878/j.issn.1006-9895.2006.05.16

Keywords: 集合卡尔曼平滑,集合卡尔曼滤波,空气质量,污染源,反演模拟,资料同化

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Abstract:

此文目的是讨论污染源反演问题的统计方法.基于Bayes估计理论,该文将资料同化中的集合平滑、集合卡尔曼平滑和集合卡尔曼滤波应用在污染源反演问题中.在详细给出污染源反演的集合平滑、集合卡尔曼平滑和集合卡尔曼滤波的严格数学表达后,用一个简单的模型演示了集合卡尔曼平滑和集合卡尔曼滤波在污染源反演中的可行性,并且通过对比理想试验结果比较了集合卡尔曼平滑和集合卡尔曼滤波方法在反演污染源排放的效果,讨论了观测误差和污染源先验误差估计对反演结果的影响.试验结果表明在观测间隔小和观测误差小的情况下,集合卡尔曼滤波和集合卡尔曼平滑都可以有效地反演出随时间变化的污染源排放.当观测误差增大时,集合卡尔曼滤波和集合卡尔曼平滑的反演效果都有一定降低,但是反演误差的增加少于观测误差的增加,同时集合卡尔曼平滑(EnsembleKalmansmoother,简称EnKS)对观测误差比集合卡尔曼滤波(EnsembleKalmanfilter,简称EnKF)更为敏感.当观测时间间隔较大时,EnKF不能对没有观测时的污染源排放进行估计,仅能对有观测时的污染源排放进行较好的反演.而EnKS可以利用观测对观测时刻前的污染源排放进行反演,因此其效果明显好于EnKF,并且在观测时间间隔较大的情况下依然可以较好地反演出污染源排放.试验结果还显示污染源排放的先验误差估计对反演的结果有较大影响.

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