用AI系统性修正气象预报偏差，提升多变量预测精度。
How to systematically develop an effective AI-based bias correction model?
- 融合动态气候归一化与时空因果约束的ConvLSTM结构
- 对气温、风速等变量实现最高20%的均方误差降低
- 轻量模型支持跨变量快速适配，适合气象建模与应用
本研究提出ReSA-ConvLSTM，一种用于数值天气预报（NWP）系统性偏差修正的人工智能框架。通过整合动态气候归一化、带时间因果约束的ConvLSTM以及残差自注意力机制，构建了ECMWF预报与ERA5再分析数据间的物理感知非线性映射关系。基于1981-2021年共41年的全球大气数据，该框架显著降低了2米气温（T2m）、10米风速（U10/V10）和海平面气压（SLP）的系统性偏差，在1-7天预报中相较操作型ECMWF输出最多减少20%的均方根误差（RMSE）。模型参数量仅10.6M，具备轻量化特性，支持多变量通用迁移，跨变量修正可使重训练时间减少85%，并通过校正后的边界条件提升海洋模型性能。消融实验表明，引入变量特征显著提升校正效果，说明考虑变量特性的建模有助于增强预报能力。
原文摘要 · Abstract (English)
This study introduces ReSA-ConvLSTM, an artificial intelligence (AI) framework for systematic bias correction in numerical weather prediction (NWP). We propose three innovations by integrating dynamic climatological normalization, ConvLSTM with temporal causality constraints, and residual self-attention mechanisms. The model establishes a physics-aware nonlinear mapping between ECMWF forecasts and ERA5 reanalysis data. Using 41 years (1981-2021) of global atmospheric data, the framework reduces systematic biases in 2-m air temperature (T2m), 10-m winds (U10/V10), and sea-level pressure (SLP), achieving up to 20% RMSE reduction over 1-7 day forecasts compared to operational ECMWF outputs. The lightweight architecture (10.6M parameters) enables efficient generalization to multiple variables and downstream applications, reducing retraining time by 85% for cross-variable correction while improving ocean model skill through bias-corrected boundary conditions. The ablation experiments demonstrate that our innovations significantly improve the model's correction performance, suggesting that incorporating variable characteristics into the model helps enhance forecasting skills.
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