Analog ensemble data assimilation and a method for constructing analogs with variational autoencoders
Analog ensemble data assimilation and a method for constructing analogs with variational autoencoders
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DOI：
10.1002/qj.3910
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发表时间：
2020-06
影响因子：
8.9
通讯作者：
I. Grooms
中科院分区：
文献类型：
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作者：
I. Grooms
建议使用预测平均值的类似物来生成用于集合最优插值（EnOI）或集合变分（EnVar）方法的扰动集合。提出了一种利用变分自编码器（VAE;一种机器学习方法）构造模拟的新方法。使用来自目录的类似物（AnEnOI）和使用构建的类似物（cAnEnOI）的所得模拟方法在多尺度Lorenz 1996模型的背景下进行测试，其中标准EnOI和用于比较的系综平方根滤波器。使用来自中等大小目录的类似物可以提高EnOI的性能，但由于目录大小的增加，边际改善有限。使用构造的模拟（cAnEnOI）的方法被发现执行以及一个完整的合奏平方根滤波器，并在很宽的范围内的调谐参数是强大的。
It is proposed to use analogs of the forecast mean to generate an ensemble of perturbations for use in ensemble optimal interpolation (EnOI) or ensemble variational (EnVar) methods. A new method of constructing analogs using variational autoencoders (VAEs; a machine learning method) is proposed. The resulting analog methods using analogs from a catalog (AnEnOI), and using constructed analogs (cAnEnOI), are tested in the context of a multiscale Lorenz‐‘96 model, with standard EnOI and an ensemble square root filter for comparison. The use of analogs from a modestly‐sized catalog is shown to improve the performance of EnOI, with limited marginal improvements resulting from increases in the catalog size. The method using constructed analogs (cAnEnOI) is found to perform as well as a full ensemble square root filter, and to be robust over a wide range of tuning parameters.