A novel deep learning-based framework for five-day regional weather forecasting

Deep learning-based methods have become alternatives to traditional numerical weather prediction systems, offering faster computation and the ability to utilize large historical datasets. However, the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge. In this work, we propose three key solutions: (1) motivated by the need to improve model performance in data-scarce regional forecasting scenarios, we innovatively apply semantic segmentation models, to better capture spatiotemporal features and improve prediction accuracy; (2) recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness, we introduce a novel learnable Gaussian noise mechanism that allows the model to adaptively optimize perturbations for different locations, ensuring more effective learning; and (3) to address the issue of error accumulation in autoregressive prediction, as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction, we propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance. Our method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition. Ablation experiments further validate the effectiveness of each component, highlighting their contributions to enhancing prediction performance. 摘要 深度学习逐渐替代传统数值天气预报 (NWP) 系统, 但在数据有限的中期天气预报中仍面临挑战。为此, 本文提出三项创新:首先, 引入语义分割模型增强时空特征捕捉能力, 提高预测精度;其次, 设计可学习的高斯噪声机制, 解决过拟合问题并突破传统噪声增强的局限性;最后, 提出级联预测方法, 平衡预测精度与误差控制, 缓解自回归预测的误差累积问题。该方法在华东区域AI中期气象预报竞赛中表现优异, 实验验证了各模块的有效性, 其中语义分割降低温度预测误差9.3%, 噪声机制提升降水预测F1-score 6.8%, 级联策略减少风速预测均方误差12.5%。此研究为数据受限的区域气象预报提供了新路径。