首页> 外文期刊>Quarterly Journal of the Royal Meteorological Society >Maximum likelihood estimation of error covariances in ensemble-based filters and its application to a coupled atmosphere-ocean model
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Maximum likelihood estimation of error covariances in ensemble-based filters and its application to a coupled atmosphere-ocean model

机译:基于集合的滤波器中误差协方差的最大似然估计及其在大气-海洋耦合模型中的应用

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We propose a method for estimating optimal error covariances in the context of sequential assimilation, including the case where both the system equation and the observation equation are nonlinear. When the system equation is nonlinear, ensemble-basedfiltering methods such as the ensemble Kalman filter (EnKF) are widely used to deal directly with the nonlinearity. The present approach for covariance optimization is a maximum likelihood estimation carried out by approximating the likelihood with theensemble mean. Specifically, the likelihood is approximated as the sample mean of the likelihood of each member of the ensemble. To evaluate the sampling error of the proposed ensemble-approximated likelihood, we construct a method for examining the statistical significance using the bootstrap method without extra ensemble computation. We apply the proposed methods to an EnKF experiment where TOPEX/POSEIDON altimetry observations are assimilated into an intermediate coupled model, which is nonlinear, and estimate the optimal parameters that specify the covariances of the system noise and observation noise. Using these optimal covariance parameters, we examine the estimates by the EnKF and the ensemble Kalman smoother (EnKS). The effect of smoothing decreases by 1/e approximately one year after the filtering step. One of the properties of the smoothed estimate is that westerly wind anomalies over the western Pacific are not reproduced around the period of an El Nino event, while those over the centralPacific are strengthened. From additional experiments, we find that (1) the westerly winds in the western Pacific are phenomena outside of the coupled model and are not necessary to model El Niiio, (2) the model El Niiio is maintained by the westerlies over the central Pacific, and (3) the modelled evolution process of the sea-surface temperature (SST) requires improvement to reproduce the westerly winds over the western Pacific.
机译:我们提出了一种在顺序同化的情况下估计最佳误差协方差的方法,包括系统方程和观测方程都是非线性的情况。当系统方程为非线性时,基于集合的滤波方法(例如集合卡尔曼滤波器(EnKF))被广泛用于直接处理非线性。用于协方差优化的当前方法是通过将似然与总体平均值近似来执行的最大似然估计。具体而言,似然度近似为集合中每个成员的似然度的样本均值。为了评估所提出的集合近似似然的抽样误差,我们构造了一种使用自举方法检查统计显着性的方法,而无需进行额外的集合计算。我们将提出的方法应用于EnKF实验,在该实验中,将TOPEX / POSEIDON高程观测同化为非线性的中间耦合模型,并估计了指定系统噪声和观测噪声协方差的最佳参数。使用这些最佳协方差参数,我们检查了EnKF和集合卡尔曼平滑器(EnKS)的估计。滤波步骤大约一年后,平滑效果降低了1 / e。平滑估计的特征之一是,在厄尔尼诺事件发生期间,未再现西太平洋上空的西风异常,而中太平洋上空的西风异常则得到加强。通过其他实验,我们发现(1)西太平洋的西风是耦合模型之外的现象,对于El Niiio的模型不是必需的;(2)El Niiio的模型是由太平洋中部的西风维持的, (3)建模的海面温度(SST)演化过程需要改进以重现西太平洋上的西风。

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