用机器学习技术构造用于数据同化的补丁模拟系综
Machine Learning Techniques to Construct Patched Analog Ensembles for Data Assimilation
摘要：
[Grooms QJRMS，2020]中介绍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合成员，作为构建的模拟集合最佳插值（cAnEnOI）。具体来说，我们研究了这种方法的机器学习组件的一般和变分自编码器，并结合了数据同化中的构造模拟和集合最优插值的思想。为了扩展cAnEnOI的可扩展性，用于数据同化复杂的动力学模型，我们建议使用修补方案将全球空间域划分为可消化的块。使用补丁使得训练生成模型成为可能，并且具有能够在生成步骤期间利用并行性的额外好处。测试这种新算法的一维玩具模型，我们发现，更大的补丁大小使它更难训练一个准确的生成模型（即模型的重建误差小），而相反的数据同化性能提高在更大的补丁大小。因此，存在最佳点，其中斑块大小足够大以实现良好的数据同化性能，但不太大以至于难以训练精确的生成模型。在我们的测试中，新的修补cAnEnOI方法优于原始（未修补）cAnEnOI，以及[Grooms QJRMS，2020]的总体平方根滤波器结果。
英文摘要：
Using generative models from the machine learning literature to create artificial ensemble members for use within data assimilation schemes has been introduced in [Grooms QJRMS, 2020] 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 [Grooms QJRMS, 2020].
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