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<p>Given the complexity of the atmospheric system, current numerical weather prediction models struggle with accurate forecasts. Here we present FengWu, an Artificial-Intelligence-driven global medium-range forecasting system employing multi-modal and multi-task learning to simulate atmospheric dynamics at 0.25° spatial resolution across 13 pressure levels. To decrease the error accumulation problem, a replay buffer mechanism has been implemented with high computational efficiency. These enhancements allow FengWu to outperform deterministic forecasts produced by European Centre for Medium-Range Weather Forecasts High-Resolution Model, Pangu-Weather, and GraphCast. Additionally, to address predictive uncertainty, we develop FengWu-Ensemble, using conditional diffusion model that generates reliable multi-member forecasts based on deterministic predictions. Comparative evaluations against the Integrated Forecasting System Ensemble show that FengWu-Ensemble achieves superior performance across multiple meteorological variables and evaluation metrics. These results indicate that FengWu holds strong potential for improving both deterministic and probabilistic weather forecasting.</p>

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The operational medium-range deterministic weather forecasting can be extended beyond a 10-day lead time

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月3日

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  • Kang Chen,
  • Tao Han,
  • Fenghua Ling,
  • Junchao Gong,
  • Lei Bai,
  • Xinyu Wang,
  • Jing-Jia Luo,
  • Ben Fei,
  • Wenlong Zhang,
  • Xi Chen,
  • Leiming Ma,
  • Tianning Zhang,
  • Rui Su,
  • Yuanzheng Ci,
  • Bin Li,
  • Xiaokang Yang,
  • Wanli Ouyang

摘要

Given the complexity of the atmospheric system, current numerical weather prediction models struggle with accurate forecasts. Here we present FengWu, an Artificial-Intelligence-driven global medium-range forecasting system employing multi-modal and multi-task learning to simulate atmospheric dynamics at 0.25° spatial resolution across 13 pressure levels. To decrease the error accumulation problem, a replay buffer mechanism has been implemented with high computational efficiency. These enhancements allow FengWu to outperform deterministic forecasts produced by European Centre for Medium-Range Weather Forecasts High-Resolution Model, Pangu-Weather, and GraphCast. Additionally, to address predictive uncertainty, we develop FengWu-Ensemble, using conditional diffusion model that generates reliable multi-member forecasts based on deterministic predictions. Comparative evaluations against the Integrated Forecasting System Ensemble show that FengWu-Ensemble achieves superior performance across multiple meteorological variables and evaluation metrics. These results indicate that FengWu holds strong potential for improving both deterministic and probabilistic weather forecasting.