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.

中文翻译：

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机器学习用于模型错误推理和校正

模型误差是使用最新，全面的高分辨率普通环流模型进行数值天气预报（NWP）和气候预测时提高准确性和可靠性的主要障碍之一。在数据同化框架中，在弱约束4D-Var背景下的最新进展表明，有可能估计和校正在平流层中短期预报范围内发展的大部分系统模型误差。最近在机器学习/深度学习技术上的兴趣激增，是由于它们在不同的应用领域取得了巨大的成功。这就提出了一个问题，即运行中的NWP和气候预测中的模型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 我们旨在开始对此问题给出答案。具体而言，我们表明，人工神经网络（ANN）可以重现在欧洲中型天气预报中心（ECMWF）IFS模型的运行配置中使用弱约束4D-Var获得的主要结果。我们表明，在弱约束4D-Var框架内使用ANN模型有潜力将弱约束方法学用于模型误差校正的适用性扩展到整个大气柱。最后，我们讨论了NWP核心任务中机器学习/深度学习技术的潜力和局限性。尤其是，