基于地球系统模式的局地化粒子滤波器与集合卡尔曼滤波器同化实验

Data assimilation experiments using localized particle filter and ensemble Kalman filter with community earth system model

粒子滤波器(PF)是一种非常具有应用前景的非线性资料同化方法。但由于其算法本身存在的粒子退化问题,目前尚未被广泛地应用于大型地球物理模式。目前主流的集合同化系统仍然倾向于使用集合卡尔曼滤波器(EnKF)及其衍生方法。一种新近被提出的局地化粒子滤波器(LPF)在经典的粒子滤波器算法中引入局地化技术,可以使用较小的计算成本有效地避免退化问题,具有非常大的业务应用潜力。本文在全耦合的通用地球系统模式中开展了LPF和EnKF的同化实验,同化资料为模拟的卫星海表温度资料。着重考察了不同局地化参数对两种方法的不同影响,对比了局地化粒子滤波器与集合卡尔曼滤波器的同化效果差异。比较的结果表明,LPF的同化效果对于局地化参数的选择非常敏感,在使用最优局地化参数的条件下,LPF能达到与EnKF相当甚至优于后者的同化效果,并具有较大的改进空间。

Particle filter(PF)is a very promising nonlinear data assimilation method.However,due to the particle degeneracy problem,it has not been widely used in large geophysical models.In contrast,the ensemble Kalman filter(EnKF)and its derivative methods have been widely used in operational data assimilation systems in recent years.A newly proposed local particle filter(LPF)which employs the localization technique in particle filter,can effectively avoid the degeneracy problem with low computational costs and has great potential for practical applications.In this paper,data assimilation experiments using LPF and EnKF are conducted in a fully coupled Community earth system model.The sythetic satellite sea surface temperature data are assimilated with each method.Different impact of local parameters on each method is investigated,and the data assimilation performances of LPF and EnKF are compared.The comparison results show that the performance of LPF is more sensitive to localization parameter.With the optimal localization strategy,it is shown that LPF can be better than EnKF,and have a potential to be further improved.