The operational medium-range deterministic weather forecasting can be extended beyond a 10-day lead time
Abstract 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 atmospheri...
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Communications Earth & Environment |
| Online Access: | https://doi.org/10.1038/s43247-025-02502-y |
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| Summary: | Abstract 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. |
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| ISSN: | 2662-4435 |