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An application of the localized weighted ensemble Kalman filter for ocean data assimilation
Quarterly Journal of the Royal Meteorological Society ( IF 3.0 ) Pub Date : 2020-05-17 , DOI: 10.1002/qj.3824
Yan Chen 1 , Weimin Zhang 1, 2 , Pinqiang Wang 1
Affiliation  

We presented a new local particle filter named the localized weighted ensemble Kalman filter (LWEnKF), which was tested and verified using a simple high‐dimensional Lorenz 96 model. A revised LWEnKF, the proposal weights calculation of which is modified through localization to prevent filter degeneracy for real geophysical models, is explored further in this article and shows lots of potential in the implementation of real complex models. For geophysical models, the ocean dynamics changes slowly compared with that of the atmosphere. With a relatively low resolution, it is weakly nonlinear in the surface layers of the ocean model used in this article, which fits the linear and Gaussian assumptions of the EnKF but could be a challenge for particle filters in the data assimilation process. With only 50 particles, the LWEnKF assimilates the sea‐surface temperature (SST), sea‐surface height (SSH), temperature, and salinity profiles with affordable computational cost, providing a reasonable forecast. Moreover, the LWEnKF is compared with the ensemble Kalman filter (EnKF) and the local particle filter (PF). For observed variables, the LWEnKF performs comparably to the EnKF, as the observation operator is linear. For unobserved variables, the LWEnKF provides more accurate forecasts than the EnKF, since the latter considers only the correlations, while the former considers higher‐order moments. The local PF ensemble does not converge to the observed solution in an ample amount of time in this study, which needs further investigation.

中文翻译:

局部加权集合卡尔曼滤波在海洋数据同化中的应用

我们提出了一个新的局部粒子滤波器,称为局部加权集成卡尔曼滤波器(LWEnKF),它使用简单的高维Lorenz 96模型进行了测试和验证。本文进一步探讨了修订的LWEnKF,该建议的权重计算通过本地化进行了修改,以防止实际地球物理模型的过滤器退化,并显示了在实现实际复杂模型中的许多潜力。对于地球物理模型,海洋动力学与大气动力学相比变化缓慢。由于分辨率相对较低,因此本文使用的海洋模型的表层是弱非线性的,这符合EnKF的线性和高斯假设,但对于数据同化过程中的粒子过滤器可能会构成挑战。只有50个粒子,LWEnKF以可承受的计算成本来同化海面温度(SST),海面高度(SSH),温度和盐度剖面,从而提供合理的预测。此外,将LWEnKF与集成卡尔曼滤波器(EnKF)和局部粒子滤波器(PF)进行比较。对于观测变量,由于观测算子是线性的,因此LWEnKF的性能与EnKF相当。对于未观察到的变量,LWEnKF提供的预测要比EnKF准确,因为后者仅考虑相关性,而前者考虑高阶矩。在此研究中,本地PF集合在足够长的时间内没有收敛到观察到的解,这需要进一步研究。提供合理的预测。此外,将LWEnKF与集成卡尔曼滤波器(EnKF)和局部粒子滤波器(PF)进行比较。对于观测变量,由于观测算子是线性的,因此LWEnKF的性能与EnKF相当。对于未观察到的变量,LWEnKF提供的预测要比EnKF准确,因为后者仅考虑相关性,而前者考虑高阶矩。在此研究中,本地PF集合在足够长的时间内没有收敛到观察到的解,这需要进一步研究。提供合理的预测。此外,将LWEnKF与集成卡尔曼滤波器(EnKF)和局部粒子滤波器(PF)进行比较。对于观测变量,由于观测算子是线性的,因此LWEnKF的性能与EnKF相当。对于未观察到的变量,LWEnKF提供的预测要比EnKF准确,因为后者仅考虑相关性,而前者考虑高阶矩。在此研究中,本地PF集合在足够长的时间内没有收敛到观察到的解,这需要进一步研究。LWEnKF比EnKF提供更准确的预测,因为后者仅考虑相关性,而前者考虑高阶矩。在此研究中,本地PF集合在足够长的时间内没有收敛到观察到的解,这需要进一步研究。LWEnKF比EnKF提供更准确的预测,因为后者仅考虑相关性,而前者考虑高阶矩。在此研究中,本地PF集合在足够长的时间内没有收敛到观察到的解,这需要进一步研究。
更新日期:2020-05-17
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