Development of an offline and online hybrid model for the Integrated Forecasting System

Abstract

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 strengths, notably accuracy and low computational requirements, but also their weaknesses: 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 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.