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 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.

References in corpus (13)

• FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
• Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model
• Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model
• Using machine learning to correct model error in data assimilation and forecast applications
• Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization
• Supervised learning from noisy observations: Combining machine-learning techniques with data assimilation
• Applying machine learning to improve simulations of a chaotic dynamical system using empirical error correction
• GenCast: Diffusion-based ensemble forecasting for medium-range weather
• A posteriori learning for quasi-geostrophic turbulence parametrization
• Super-resolution data assimilation
• An iterative ensemble Kalman filter in presence of additive model error
• Online learning of both state and dynamics using ensemble Kalman filters
• State, global and local parameter estimation using local ensemble Kalman filters: applications to online machine learning of chaotic dynamics