Online model error correction with neural networks: application to the Integrated Forecasting System
<|reference_start|>Online model error correction with neural networks: application to the Integrated Forecasting System: 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.<|reference_end|>
@article{farchi2024online,
    title={Online model error correction with neural networks: application to the
  Integrated Forecasting System},
    author={Alban Farchi, Marcin Chrust, Marc Bocquet, Massimo Bonavita},
    journal={arXiv preprint arXiv:2403.03702},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.03702},
    primaryClass={stat.ML cs.LG}
}