Title: Machine Learning Techniques to Construct Patched Analog Ensembles for Data Assimilation Show Chinese title

Title: 用于数据同化的分块模拟集合构建的机器学习技术

Abstract: 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]. Show Chinese abstract

Abstract: 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，已在[Grooms QJRMS, 2020]中引入，称为构造模拟集合最优插值（cAnEnOI）。 具体而言，我们研究了通用和变分自编码器作为该方法机器学习组件的选项，并在数据同化部分结合了构造模拟和集合最优插值的思想。 为了扩展cAnEnOI在复杂动力模型上的可扩展性，我们提出使用补丁方案将全球空间域划分为可处理的块。 使用补丁使得训练生成模型成为可能，并且在生成步骤中能够利用并行性。 在一个一维玩具模型上测试这个新算法，我们发现较大的补丁尺寸会使训练准确的生成模型变得困难（即，其重建误差较小的模型），而相反地，数据同化性能在较大的补丁尺寸下得到改善。 因此存在一个最佳点，其中补丁尺寸足够大以实现良好的数据同化性能，但又不至于大到难以训练出准确的生成模型。 在我们的测试中，新的带补丁的cAnEnOI方法优于原始（无补丁）的cAnEnOI，以及[Grooms QJRMS, 2020]中的集合平方根滤波结果。