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<p>The rapid advancement of artificial intelligence models is expanding the frontiers of weather forecasting, underscoring the need for comprehensive evaluations in specific applications to ensure their effective use and guide future development. Here we benchmark five state-of-the-art artificial intelligence models for forecasting atmospheric rivers, assessing both meteorological fields and atmospheric river-related metrics across global and regional scales. Results show that FuXi achieves the best performance at a 10-day lead time for meteorological fields and atmospheric river forecasts globally. However, regional assessments reveal that the model incorporating numerical components—NeuralGCM—performs better in predicting atmospheric river intensity. Two case studies along the North and South American coasts further highlight NeuralGCM’s superior ability to predict atmospheric river shapes and intensities at 10-day lead times. Nonetheless, accurately predicting atmospheric river landfall locations beyond one week remains a challenge, emphasizing the need for refinement of these models for region-specific forecasts in future applications.</p>

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Global performance benchmarking of artificial intelligence models in atmospheric river forecasting

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月13日

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  • Lujia Zhang,
  • Mengqian Lu,
  • Qing Bao,
  • Yang Zhao,
  • Jing Yang

摘要

The rapid advancement of artificial intelligence models is expanding the frontiers of weather forecasting, underscoring the need for comprehensive evaluations in specific applications to ensure their effective use and guide future development. Here we benchmark five state-of-the-art artificial intelligence models for forecasting atmospheric rivers, assessing both meteorological fields and atmospheric river-related metrics across global and regional scales. Results show that FuXi achieves the best performance at a 10-day lead time for meteorological fields and atmospheric river forecasts globally. However, regional assessments reveal that the model incorporating numerical components—NeuralGCM—performs better in predicting atmospheric river intensity. Two case studies along the North and South American coasts further highlight NeuralGCM’s superior ability to predict atmospheric river shapes and intensities at 10-day lead times. Nonetheless, accurately predicting atmospheric river landfall locations beyond one week remains a challenge, emphasizing the need for refinement of these models for region-specific forecasts in future applications.