论文标题
模拟集合数据同化和一种用变异自动编码器构建类似物的方法
Analog ensemble data assimilation and a method for constructing analogs with variational autoencoders
论文作者
论文摘要
建议使用预测均值的类似物来生成一组扰动，以用于集合最佳插值（ENOI）或集合变分（ENGAR）方法。提出了一种使用变异自动编码器（VAE；机器学习方法）构建类似物的新方法。使用来自目录（Anenoi）的类似物以及使用构造的模拟（CANENOI）的模拟方法，在具有标准ENOI和集合方形根过滤器的多尺度Lorenz-96模型的背景下进行了测试。显示出来自适度目录的类似物的使用可改善ENOI的性能，由于目录尺寸的增加而导致边际改进有限。发现使用构造类似物（Canenoi）的方法可以像完整的集合方形根滤波器一样执行，并且在广泛的调谐参数上具有稳健性。
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.
