🤖 AI Summary
This study addresses the rapid degradation of forecast skill in chaotic systems and the limitations of traditional data assimilation (DA) by exploring the integration of machine learning (ML) with DA. Methodologically, it combines deep learning models, DA algorithms, and numerical weather prediction techniques to optimize system state estimation through the effective fusion of observational data and model forecasts. The core contribution lies in providing a pioneering review of the emerging ML-DA paradigm, delineating its central themes and methodological frameworks. This work offers a novel methodological perspective for state estimation in dynamical systems and establishes a solid foundation for enhancing predictive capabilities in complex systems, such as the atmosphere, while guiding future research directions.
📝 Abstract
Data assimilation (DA) is the process of combining forecasts from a model with observations in order to optimally estimate the state of a system. This is critical for chaotic systems, such as the atmosphere, since if observations are not continually assimilated the model will quickly lose skill. DA is routinely performed (usually every 6 hours) at operational forecasting centres around the world.
In this article we discuss the interface of machine learning (ML) and DA. This is still an emerging and quickly developing field, and this article tries to give an overview of some of the main topics and methods.