The operational medium-range deterministic weather forecasting can be extended beyond a 10-day lead time
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
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. A new deep learning-based global medium-range weather forecasting model outperforms existing machine learning models and the European Centre for Medium-Range Weather Forecasts model, producing accurate global weather forecasts beyond ten days.