Machine learning techniques to construct patched analog ensembles for data assimilation
Machine learning techniques to construct patched analog ensembles for data assimilation
复制标题
用于构建用于数据同化的修补模拟集合的机器学习技术
DOI：
10.1016/j.jcp.2021.110532
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发表时间：
2021
影响因子：
4.1
通讯作者：
Grooms, Ian
中科院分区：
文献类型：
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作者：
Yang, L. Minah;Grooms, Ian
Grooms (2021) [1] 中引入了使用机器学习文献中的生成模型来创建用于数据同化方案的人工集成成员，作为构造模拟集成最优插值 (cAnEnOI)。具体来说，我们研究了该方法的机器学习组件的通用和变分自动编码器，并在数据同化部分中结合了构造类比和集成最优插值的思想。为了扩展 cAnEnOI 的可扩展性以用于复杂动力学模型的数据同化，我们建议使用修补方案将全局空间域划分为可消化的块。使用补丁可以训练生成模型，并且具有能够在生成步骤中利用并行性的额外好处。在一维玩具模型上测试这种新算法，我们发现较大的块尺寸使得训练准确的生成模型（即重建误差较小的模型）变得更加困难，而相反，数据同化性能在较大的块尺寸下得到改善。因此，存在一个最佳点，即块大小足够大以实现良好的数据同化性能，但又不能大到难以训练准确的生成模型。在我们的测试中，新的修补 cAnEnOI 方法优于原始（未修补）cAnEnOI 以及 [1] 中的集成平方根滤波器结果。
Using generative models from the machine learning literature to create artificial ensemble members for use within data assimilation schemes has been introduced in Grooms (2021) [1] as constructed analog ensemble optimal interpolation (cAnEnOI). Specifically, we study general and variational autoencoders for the machine learning component of this method, and combine the ideas of constructed analogs and ensemble optimal interpolation in the data assimilation piece. To extend the scalability of cAnEnOI for use in data assimilation on complex dynamical models, we propose using patching schemes to divide the global spatial domain into digestible chunks. Using patches makes training the generative models possible and has the added benefit of being able to exploit parallelism during the generative step. Testing this new algorithm on a 1D toy model, we find that larger patch sizes make it harder to train an accurate generative model (i.e. a model whose reconstruction error is small), while conversely the data assimilation performance improves at larger patch sizes. There is thus a sweet spot where the patch size is large enough to enable good data assimilation performance, but not so large that it becomes difficult to train an accurate generative model. In our tests the new patched cAnEnOI method outperforms the original (unpatched) cAnEnOI, as well as the ensemble square root filter results from [1].