Pr\'evisions m\'et\'eorologiques bas\'ees sur l'intelligence artificielle : une r\'evolution peut en cacher une autre
Zied Ben-Bouallegue, Mariana C A Clare, Matthieu Chevallier

TL;DR
AI-driven weather forecasting shows promising deterministic results but faces challenges in realism, suggesting a shift towards probabilistic methods could improve forecast reliability and address the realism-performance trade-off.
Contribution
This paper analyzes the realism and performance of AI-based weather forecasts, highlighting the need for probabilistic approaches to enhance forecast credibility.
Findings
Deterministic AI forecasts show promising results.
Realism of AI forecasts is questioned and complex.
Probabilistic methods may resolve realism/performance issues.
Abstract
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 pr\'evision m\'et\'eorologique avec l'utilisation…
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Taxonomy
TopicsComputability, Logic, AI Algorithms
