用机器学习模型替代低精度模型，提升气象预测精度与速度。
Balancing Accuracy and Speed: A Multi-Fidelity Ensemble Kalman Filter with a Machine Learning Surrogate Model
- 用机器学习代理模型替代传统低分辨率模型，构建多保真度卡尔曼滤波器。
- 在相同计算预算下，相比纯高精度模型或纯低精度模型，精度显著提升。
- 适合需要快速高精度预测的气象、海洋等领域的实际应用。
当前越来越多机器学习（ML）代理模型被用于替代计算成本高昂的物理模型。本文研究一种多保真度集合卡尔曼滤波器（MF-EnKF），其中低保真度模型采用机器学习代理模型，而非传统的低分辨率或降阶模型。该方法利用少量昂贵的全模型运行与大量廉价但精度较低的机器学习模型运行构成集合。通过保留原始物理模型，相比完全用机器学习模型替代，获得更高精度；同时在相同计算预算下实现更优的精度表现。机器学习代理模型的精度与低分辨率模型相当或更优，但可提供更大加速比。本方法有效扩大了集合卡尔曼滤波器中的有效集合规模，提升了初始状态估计精度，从而改善气象学与海洋学等领域的预测效果。
原文摘要 · Abstract (English)
Currently, more and more machine learning (ML) surrogates are being developed for computationally expensive physical models. In this work we investigate the use of a Multi-Fidelity Ensemble Kalman Filter (MF-EnKF) in which the low-fidelity model is such a machine learning surrogate model, instead of a traditional low-resolution or reduced-order model. The idea behind this is to use an ensemble of a few expensive full model runs, together with an ensemble of many cheap but less accurate ML model runs. In this way we hope to reach increased accuracy within the same computational budget. We investigate the performance by testing the approach on two common test problems, namely the Lorenz-2005 model and the Quasi-Geostrophic model. By keeping the original physical model in place, we obtain a higher accuracy than when we completely replace it by the ML model. Furthermore, the MF-EnKF reaches improved accuracy within the same computational budget. The ML surrogate has similar or improved accuracy compared to the low-resolution one, but it can provide a larger speed-up. Our method contributes to increasing the effective ensemble size in the EnKF, which improves the estimation of the initial condition and hence accuracy of the predictions in fields such as meteorology and oceanography.
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