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.

通过使用神经网络在欧洲中程天气预报中的操作性集成预测系统（IFS）中开发模型误差校正来改进天气预报的准确性，并通过在线训练进一步提高混合模型的准确性。