Analog ensemble data assimilation in a quasigeostrophic coupled model
Analog ensemble data assimilation in a quasigeostrophic coupled model
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DOI：
10.1002/qj.4446
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发表时间：
2023-03
影响因子：
8.9
通讯作者：
I. Grooms;Camille Renaud;Z. Stanley;L. Minah Yang
中科院分区：
文献类型：
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作者：
I. Grooms;Camille Renaud;Z. Stanley;L. Minah Yang
集合预报在许多资料同化方法的计算成本中占主导地位，特别是对于高分辨率和耦合模式。在成本过高的情况下，可以使用低成本模型或低成本数据同化方法，或两者兼而有之。集合最优插值（Enclamentoptimal interpolation，EnOI）是低成本集合数据同化方法的一个经典例子，它用单个预报代替集合预报，然后通过添加来自气候学的扰动来构建关于该单个预报的集合。这项研究开发了低成本的集合数据同化方法，该方法将扰动添加到单个预报中，其中扰动是从单个模式预报的类似物中获得的。这些类似物可以从模型状态的目录中找到，使用目录中模型状态的线性组合构建，或者使用生成机器学习方法构建。四个模拟集合数据同化方法，包括两个新的，比较EnOI的背景下，耦合模式的中间复杂性：Q-GCM。根据方法和物理变量的不同，模拟方法的准确度比EnOI高出40%。
The ensemble forecast dominates the computational cost of many data assimilation methods, especially for high‐resolution and coupled models. In situations where the cost is prohibitive, one can either use a lower‐cost model or a lower‐cost data assimilation method, or both. Ensemble optimal interpolation (EnOI) is a classical example of a lower‐cost ensemble data assimilation method that replaces the ensemble forecast with a single forecast and then constructs an ensemble about this single forecast by adding perturbations drawn from climatology. This research develops lower‐cost ensemble data assimilation methods that add perturbations to a single forecast, where the perturbations are obtained from analogs of the single model forecast. These analogs can either be found from a catalog of model states, constructed using linear combinations of model states from a catalog, or constructed using generative machine‐learning methods. Four analog ensemble data assimilation methods, including two new ones, are compared with EnOI in the context of a coupled model of intermediate complexity: Q‐GCM. Depending on the method and on the physical variable, analog methods can be up to 40% more accurate than EnOI.