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Physics-based models outperform AI weather forecasts of record-breaking extremes.

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Physics-based weather models like HRES outperform AI models for extreme, record-breaking events. AI models struggle with extrapolation, underestimating event frequency and intensity, highlighting limitations for critical applications.

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Area of Science:

  • Meteorology
  • Artificial Intelligence
  • Climate Science

Background:

  • Artificial intelligence (AI) models show promise in weather forecasting, exceeding traditional numerical models on benchmarks.
  • The reliability of AI in predicting unprecedented extreme weather events remains uncertain.

Purpose of the Study:

  • To compare the performance of state-of-the-art AI weather models against a physics-based model for record-breaking extreme events.
  • To identify current limitations of AI models in forecasting extreme weather phenomena.

Main Methods:

  • Comparative analysis of AI models (GraphCast, Pangu-Weather, Fuxi) and the High Resolution Forecast (HRES) physics-based model.
  • Evaluation of forecast errors for record-breaking heat, cold, and wind events across various lead times.

Main Results:

  • The HRES physics-based model consistently outperformed AI models for record-breaking weather extremes.
  • AI models exhibited larger forecast errors for extreme events compared to HRES.
  • AI models tended to underestimate the frequency and intensity of record-breaking events, with specific biases for hot and cold records.

Conclusions:

  • Current AI weather models have limitations in extrapolating beyond training data for extreme events.
  • Physics-based models remain superior for forecasting high-impact, record-breaking weather, crucial in a warming climate.
  • Further development and verification of AI models are necessary for high-stakes applications like early warning systems.