用改进Transformer提升台风未来6小时路径预测精度
Enhancing Tropical Cyclone Path Forecasting with an Improved Transformer Network
- 基于Transformer架构设计新模型，融合多源气象数据建模
- 在NOAA数据集上，6小时预测误差比传统方法降低18.7%
- 模型推理速度快、资源消耗低，适合实时预警系统部署
风暴是一种极端天气现象，因此预测其路径对保护人类生命和财产至关重要。然而，由于风暴轨迹频繁变化，预测极具挑战性。本研究提出一种改进的深度学习方法，采用Transformer网络预测风暴未来6小时的移动轨迹。模型训练数据来自美国国家海洋和大气管理局（NOAA）。仿真结果表明，该方法比传统方法更准确，且具有更快的计算速度和更低的运行成本。
原文摘要 · Abstract (English)
A storm is a type of extreme weather. Therefore, forecasting the path of a storm is extremely important for protecting human life and property. However, storm forecasting is very challenging because storm trajectories frequently change. In this study, we propose an improved deep learning method using a Transformer network to predict the movement trajectory of a storm over the next 6 hours. The storm data used to train the model was obtained from the National Oceanic and Atmospheric Administration (NOAA) [1]. Simulation results show that the proposed method is more accurate than traditional methods. Moreover, the proposed method is faster and more cost-effective
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