In previous work, it was shown that the preservation of physical properties in the data assimilation framework can significantly reduce forecast errors. Proposed data assimilation methods, such as the quadratic programming ensemble (QPEns) that can impose such constraints on the calculation of the analysis, are computationally more expensive, severely limiting their application to high-dimensional prediction systems as found in Earth sciences. We, therefore, propose using a convolutional neural network (CNN) trained on the difference between the analysis produced by a standard ensemble Kalman filter (EnKF) and the QPEns to correct any violations of imposed constraints. In this paper, we focus on the conservation of mass and show that, in an idealised set-up, the hybrid of a CNN and the EnKF is capable of reducing analysis and background errors to the same level as the QPEns.

中文翻译：

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训练卷积神经网络以节省数据同化质量

在以前的工作中，已经表明在数据同化框架中保留物理属性可以显着减少预测误差。提议的数据同化方法（例如可以对分析的计算施加此类约束的二次编程集成（QPEns））在计算上更加昂贵，严重地限制了它们在地球科学中发现的高维预测系统中的应用。因此，我们建议使用经过卷积神经网络（CNN）训练，以对标准集成卡尔曼滤波器（EnKF）和QPEns产生的分析结果之间的差异进行校正，以纠正违反约束的情况。在本文中，我们着重于质量守恒，并表明在理想的设置中，