Data Assimilation for a Quasi-Geostrophic Model with Circulation-Preserving Stochastic Transport Noise
Data Assimilation for a Quasi-Geostrophic Model with Circulation-Preserving Stochastic Transport Noise
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DOI：
10.1007/s10955-020-02524-0
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发表时间：
2020-03-23
影响因子：
1.6
通讯作者：
Shevchenko, Igor
中科院分区：
文献类型：
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作者：
Cotter, Colin;Crisan, Dan;Shevchenko, Igor
本文介绍了作者在发展高维流体动力学模式基于集合的数据同化方法的项目中的最新进展。这里提出的算法是一个粒子滤波器，结合了模型简化，回火，抖动和轻推。该方法是测试一个两层准地转模式的β平面通道流与O（106）自由度，其中只有一小部分的噪音观察。该模型通过遵循在（霍尔姆in Proc R Soc A 41：20140963，2015）中引入的用于地球物理流体动力学的随机变分方法来简化，该方法作为用于导出未分辨尺度的随机参数化的框架。减少是实质性的：计算只为O（104）自由度。我们引入了一个随机的时间步进计划的两层模型，并证明其在时间上的一致性。然后，我们分析了不同的程序（回火与抖动和轻推相结合）的数据同化过程中使用的简化模式的性能的影响，以及如何观测数据的尺寸（“气象站”的数量）和数据同化步骤影响结果的准确性和不确定性。
This paper contains the latest installment of the authors' project on developing ensemble based data assimilation methodology for high dimensional fluid dynamics models. The algorithm presented here is a particle filter that combines model reduction, tempering, jittering, and nudging. The methodology is tested on a two-layer quasi-geostrophic model for a beta-plane channel flow with O(106) degrees of freedom out of which only a minute fraction are noisily observed. The model is reduced by following the stochastic variational approach for geophysical fluid dynamics introduced in (Holm in Proc R Soc A 41:20140963, 2015) as a framework for deriving stochastic parameterisations for unresolved scales. The reduction is substantial: the computations are done only for O(104) degrees of freedom. We introduce a stochastic time-stepping scheme for the two-layer model and prove its consistency in time. Then, we analyze the effect of the different procedures (tempering combined with jittering and nudging) on the performance of the data assimilation procedure using the reduced model, as well as how the dimension of the observational data (the number of "weather stations") and the data assimilation step affect the accuracy and uncertainty of the results.