Data assimilation (DA) can provide the more accurate initial state for numerical forecasting models. But traditional DA algorithms has the problem of long calculation time. This paper proposes fast data assimilation (FDA) based on machine learning. For training model, FDA uses 4DVAR, iForest, MLP, and also includes a modified model that does not require observations. This paper applies FDA in the Lorenz63 dynamical system. The experimental results show that the single analysis time of FDA is almost 524 times faster than 4DVAR. FDA greatly reduces the time of the DA process.

中文翻译：

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快速数据同化（FDA）：通过机器学习进行数据同化，以更快地优化模型状态

数据同化（DA）可以为数值预测模型提供更准确的初始状态。但是传统的DA算法存在计算时间长的问题。本文提出了一种基于机器学习的快速数据同化（FDA）。对于培训模型，FDA使用4DVAR，iForest，MLP，并且还包括不需要观察的改进模型。本文将FDA应用于Lorenz63动力学系统。实验结果表明，FDA的单次分析时间比4DVAR快524倍。FDA大大减少了DA流程的时间。