A comparison of the equivalent weights particle filter and the local ensemble transform Kalman filter in application to the barotropic vorticity equation
A comparison of the equivalent weights particle filter and the local ensemble transform Kalman filter in application to the barotropic vorticity equation
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DOI：
10.3402/tellusa.v68.30466
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发表时间：
2016-01-01
影响因子：
2
通讯作者：
Browne, Philip A.
中科院分区：
文献类型：
--
作者：
Browne, Philip A.
在高维系统中工作的数据同化方法对地球科学的许多领域至关重要：气象学，海洋学，气候科学等。等效权重粒子滤波器（EWPF）已被设计用于，最近被证明可扩展到，这些社区使用的问题。本文对EWPF与已建立并广泛使用的局部集合变换卡尔曼滤波（LETKF）进行了系统的比较。这两种方法都适用于正压涡度方程的不同网络的观测。在所有情况下，它被发现，LETKF产生较低的根均方误差比EWPF。的EWPF的性能强烈依赖于所使用的轻推的形式，和一个轻推项的基础上的本地集合变换卡尔曼平滑显示，以提高滤波器的性能。这表明，EWPF必须被视为一个真正的两级滤波器，而不仅仅是其最后一步，避免重量崩溃。
Data assimilation methods that work in high-dimensional systems are crucial to many areas of the geosciences: meteorology, oceanography, climate science and so on. The equivalent weights particle filter (EWPF) has been designed for, and recently shown to scale to, problems that are of use to these communities. This article performs a systematic comparison of the EWPF with the established and widely used local ensemble transform Kalman filter (LETKF). Both methods are applied to the barotropic vorticity equation for different networks of observations. In all cases, it was found that the LETKF produced lower root mean-squared errors than the EWPF. The performance of the EWPF is shown to depend strongly on the form of nudging used, and a nudging term based on the local ensemble transform Kalman smoother is shown to improve the performance of the filter. This indicates that the EWPF must be considered as a truly two-stage filter and not only by its final step which avoids weight collapse.