Our colleagues Alban Farchi and Marcin Chrust have led an ambitious study where they 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. This neural network is initially pre-trained offline using a large dataset of operational analyses and analysis increments. Subsequently, the trained net- work 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 trained further 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 improves the accuracy of the hybrid model further in many conditions.

The paper is entitled Development of an offline and online hybrid model for the Integrated Forecasting System, is published in the QJRMS.