arXiv subjects

Matthieu Chevallier

Publications and source records attributed to Matthieu Chevallier.

2 recordsLinked to original sources

arXiv2024

Prévisions météorologiques basées sur l'intelligence artificielle : une révolution peut en cacher une autre

Artificial intelligence (AI), based on deep-learning algorithm using high-quality reanalysis datasets, is showing enormous potential for weather forecasting. In this context, the European Centre for Medium-Range Weather Forecasts (ECMWF) is developing a new forecasting system based on AI. Verification results of deterministic forecast for now are promising. However, the realism of weather forecasts based on AI is often questioned. Here, different types of realism are identified and we discuss, in particular, the relationship between structural realism and predictability of weather events. Furthermore, a statistical analysis of deterministic forecasts based on AI points to a realism/performance dilemma that a probabilistic approach should help to solve. -- L'intelligence artificielle (IA) bouleverse aujourd'hui le monde de la prévision météorologique avec l'utilisation d'algorithmes d'apprentissage profond nourris par des champs de réanalyses. Dans ce contexte, le Centre Européen pour les Prévisions Météorologiques à Moyen Terme (CEPMMT) a décidé de développer un nouveau système de prévisions resposant sur l'IA. Ces prévisions, pour le moment de type déterministe, montrent des résultats prometteurs. Toutefois, le réalisme de ce type de prévisions reposant sur l'IA est souvent questionné. Ici, nous identifions différents types de réalisme et interrogeons notamment le rapport entre réalisme structurel et prévisibilité des évênements météorologiques. Une analyse statistique de prévisions déterministes reposant sur l'IA laisse apparaitre un dilemme réalisme/performance qu'une approche probabiliste devrait aider à résoudre.

physics.ao-ph↗

arXiv2023

The rise of data-driven weather forecasting

Data-driven modeling based on machine learning (ML) is showing enormous potential for weather forecasting. Rapid progress has been made with impressive results for some applications. The uptake of ML methods could be a game-changer for the incremental progress in traditional numerical weather prediction (NWP) known as the 'quiet revolution' of weather forecasting. The computational cost of running a forecast with standard NWP systems greatly hinders the improvements that can be made from increasing model resolution and ensemble sizes. An emerging new generation of ML models, developed using high-quality reanalysis datasets like ERA5 for training, allow forecasts that require much lower computational costs and that are highly-competitive in terms of accuracy. Here, we compare for the first time ML-generated forecasts with standard NWP-based forecasts in an operational-like context, initialized from the same initial conditions. Focusing on deterministic forecasts, we apply common forecast verification tools to assess to what extent a data-driven forecast produced with one of the recently developed ML models (PanguWeather) matches the quality and attributes of a forecast from one of the leading global NWP systems (the ECMWF IFS). The results are very promising, with comparable skill for both global metrics and extreme events, when verified against both the operational analysis and synoptic observations. Increasing forecast smoothness and bias drift with forecast lead time are identified as current drawbacks of ML-based forecasts. A new NWP paradigm is emerging relying on inference from ML models and state-of-the-art analysis and reanalysis datasets for forecast initialization and model training.

physics.ao-ph↗