Machine Learning for Model Error Inference and Correction
Machine Learning for Model Error Inference and Correction
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DOI：
10.1029/2020ms002232
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发表时间：
2020-12-01
影响因子：
6.8
通讯作者：
Laloyaux, Patrick
中科院分区：
文献类型：
--
作者：
Bonavita, Massimo;Laloyaux, Patrick
模式误差是提高数值天气预报（NWP）和气候预测准确性和可靠性的主要障碍之一，这些预报采用了最先进的综合高分辨率大气环流模式。在数据同化的框架内，最近的进展弱约束4D-Var的背景下已经表明，它是可能的估计和校正的大部分系统模型误差的发展在平流层在短期预报范围。最近对机器学习/深度学习技术的兴趣激增，是由于它们在不同应用领域取得了显著成功。这就提出了一个问题，即业务数值预报和气候预测中的模式误差估计和校正是否也可以从这些技术中受益。在这项工作中，我们的目标是开始回答这个问题。具体而言，我们表明，人工神经网络（ANN）可以重现弱约束4D-Var在欧洲中期天气预报中心（ECMWF）的IFS模型的操作配置中获得的主要结果。我们发现，使用弱约束四维无功框架内的人工神经网络模型有可能扩展模型误差校正的弱约束方法的适用性，以整个大气柱。最后，我们讨论了机器学习/深度学习技术在核心NWP任务中的潜力和局限性。特别是，我们重新考虑了纯数据驱动的预测方法的基本限制，并提供了如何将机器学习技术最好地集成到当前数据同化和预测方法中的观点。
Model error is one of the main obstacles to improved accuracy and reliability in numerical weather prediction (NWP) and climate prediction conducted with state-of-the-art, comprehensive high-resolution general circulation models. In a data assimilation framework, recent advances in the context of weak-constraint 4D-Var have shown that it is possible to estimate and correct for a large fraction of systematic model error which develops in the stratosphere over short forecast ranges. The recent explosion of interest in machine learning/deep learning technologies has been driven by their remarkable success in disparate application areas. This raises the question of whether model error estimation and correction in operational NWP and climate prediction can also benefit from these techniques. In this work, we aim to start to give an answer to this question. Specifically, we show that artificial neural networks (ANNs) can reproduce the main results obtained with weak-constraint 4D-Var in the operational configuration of the IFS model of the European Centre for Medium-Range Weather Forecasts (ECMWF). We show that the use of ANN models inside the weak-constraint 4D-Var framework has the potential to extend the applicability of the weak-constraint methodology for model error correction to the whole atmospheric column. Finally, we discuss the potential and limitations of the machine learning/deep learning technologies in the core NWP tasks. In particular, we reconsider the fundamental constraints of a purely data-driven approach to forecasting and provide a view on how to best integrate machine learning technologies within current data assimilation and forecasting methods.