🧠 Abstract:
Machine Learning (ML) is increasingly influential in weather and climate prediction. Recent advances have led to fully data-driven ML models that often claim to outperform traditional physics-based systems. This episode evaluates forecasts from three leading ML models—Pangu-Weather, FourCastNet, and GraphCast—focusing on their accuracy and physical realism.

📌 Bullet points summary:

• ML models like Pangu-Weather, FourCastNet, and GraphCast fail to capture sub-synoptic and mesoscale phenomena with adequate fidelity, producing forecasts that become overly smooth over time.

• Their energy spectra diverge significantly from traditional models and reanalysis data, leading to poor representation of features below 300–400 km scales.

• They lack accurate representation of key physical balances in the atmosphere, such as geostrophic wind balance and the divergent-rotational wind ratio, affecting the realism of weather diagnostics.

• Though computationally efficient and strong in certain metrics, these models should be seen as forecast refiners rather than full-fledged atmospheric simulators or "digital twins," as they still rely heavily on traditional models for training and input.

💡 The Big Idea:
While ML models mark a significant advancement, their current limitations highlight the indispensable role of physical principles and traditional modeling in weather prediction.

📖 Citation:
Bonavita, Massimo. "On some limitations of current machine learning weather prediction models." Geophysical Research Letters 51.12 (2024): e2023GL107377. https://doi.org/10.1029/2023GL107377