论文标题
通过人工神经网络估算模型误差协方差
Estimating Model Error Covariances with Artificial Neural Networks
论文作者
论文摘要
处理系统模型错误的方法是现代数据同化系统越来越重要的组成部分，由于方法论的进步以及全球观察系统的质量和密度，近年来其有效性已提高。 ECMWF所采用的弱约束4D-VAR同化算法非常适合对模型错误在成本函数中明确考虑的模型错误的估计和校正。近年来，这导致了平流层分析的准确性的显着改善。一个保持开放的问题是关于用于弱约束4D-VAR的模型误差协方差矩阵的估计。在过去的过去，我们通过使用人工神经网络（ANN）在ECMWF同化周期中估算了缓慢变化的模型错误的鼓励，我们在这项工作中探索了ANN来采样模型误差分布并为构建模型误差率矩阵提供了另一种方法。描述了新模型误差协方差在循环同化实验中的应用结果，并讨论了ECMWF数据同化系统进一步发展的影响。
Methods to deal with systematic model errors are an increasingly important component of modern data assimilation systems and their effectiveness has increased in recent years thanks to advances in methodology and the quality and density of the global observing system. The weak constraint 4D-Var assimilation algorithm employed at ECMWF is well suited to the estimation and correction of model errors as they are explicitly accounted for in the cost function. This has led to significant improvements in recent years to the accuracy of stratospheric analyses. One question that remains open is about the estimation of the model error covariance matrix to use in weak constraint 4D-Var. Encouraged by the promising results we have obtained in the recent past through the use of Artificial Neural Networks (ANNs) to estimate slowly-varying model errors in the ECMWF assimilation cycle, we explore in this work the use of ANNs to sample the model error distribution and provide an alternative way to construct a model error covariance matrix. Results from the application of the new model error covariance in cycling assimilation experiments are described and implications for further developments of the ECMWF data assimilation system are discussed.
