In recent years, there has been significant progress in the development of fully data-driven global numerical weather prediction models. These machine learning weather prediction models have their strength, notably accuracy and low computational requirements, but also their weakness: they struggle to represent fundamental dynamical balances, and they are far from being suitable for data assimilation experiments. Hybrid modelling emerges as a promising approach to address these limitations. Hybrid models integrate a physics-based core component with a statistical component, typically a neural network, to enhance prediction capabilities. In this article, we propose to develop a model error correction for the operational Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts using a neural network. The neural network is initially pre-trained offline using a large dataset of operational analyses and analysis increments. Subsequently, the trained network is integrated into the IFS within the Object-Oriented Prediction System (OOPS) so as to be used in data assimilation and forecast experiments. It is then further trained online using a recently developed variant of weak-constraint 4D-Var. The results show that the pre-trained neural network already provides a reliable model error correction, which translates into reduced forecast errors in many conditions and that the online training further improves the accuracy of the hybrid model in many conditions.

翻译：摘要：近年来，全数据驱动的全球数值天气预报模型取得了显著进展。这类机器学习天气预报模型具有优势，尤其是在精度和低计算需求方面，但也存在不足：难以表征基本动力学平衡，且远未适用于资料同化实验。混合建模方法有望解决这些局限，其将基于物理的核心组件与统计组件（通常是神经网络）相结合以增强预测能力。本文提出为欧洲中期天气预报中心业务运行的综合预报系统（IFS）开发基于神经网络的模式误差校正方案。首先利用包含大量业务分析场和分析增量的数据集对神经网络进行离线预训练，随后将训练完成的网络集成至面向对象预报系统（OOPS）的IFS框架中，用于资料同化与预报实验。进一步采用弱约束四维变分同化（4D-Var）的最新变体对其进行在线训练。结果表明：预训练神经网络已能提供可靠的模式误差校正，在多数条件下有效降低预报误差；而在线训练可进一步提升混合模型在多种条件下的预测精度。