论文标题
结合合奏Kalman过滤器和储层计算，以预测不完美的观察和模型的时空混沌系统
Combining Ensemble Kalman Filter and Reservoir Computing to predict spatio-temporal chaotic systems from imperfect observations and models
论文作者
论文摘要
时空混沌系统的预测在各个领域都很重要，例如数值天气预测（NWP）。尽管NWP已应用了数据同化方法，但机器学习技术（例如储层计算（RC））最近被认为是预测时空混沌系统的有希望的工具。但是，尚不清楚基于机器学习对观察不完善性的预测的敏感性尚不清楚。在这项研究中，我们通过嘈杂且分布稀少的观察结果评估了RC的技能。我们通过将其应用于Lorenz 96系统的预测来进行深入比较RC和局部集合变换Kalman滤波器（LETKF）的性能。尽管RC可以完美地观察到该系统，但RC可以成功预测Lorenz 96系统，但我们发现与LetkF相比，RC容易受到观察稀疏性的影响。为了克服RC的这种局限性，我们建议将Letkf和RC结合在一起。在我们提出的方法中，该系统由RC预测，该系统学习了Letkf估计的分析时间序列。我们提出的方法可以使用嘈杂且分布稀少的观测值成功预测Lorenz 96系统。最重要的是，当基于过程的模型不完善时，我们的方法比LetkF更好。
Prediction of spatio-temporal chaotic systems is important in various fields, such as Numerical Weather Prediction (NWP). While data assimilation methods have been applied in NWP, machine learning techniques, such as Reservoir Computing (RC), are recently recognized as promising tools to predict spatio-temporal chaotic systems. However, the sensitivity of the skill of the machine learning based prediction to the imperfectness of observations is unclear. In this study, we evaluate the skill of RC with noisy and sparsely distributed observations. We intensively compare the performances of RC and Local Ensemble Transform Kalman Filter (LETKF) by applying them to the prediction of the Lorenz 96 system. Although RC can successfully predict the Lorenz 96 system if the system is perfectly observed, we find that RC is vulnerable to observation sparsity compared with LETKF. To overcome this limitation of RC, we propose to combine LETKF and RC. In our proposed method, the system is predicted by RC that learned the analysis time series estimated by LETKF. Our proposed method can successfully predict the Lorenz 96 system using noisy and sparsely distributed observations. Most importantly, our method can predict better than LETKF when the process-based model is imperfect.
