fdata-03-00001-g0004_Tropical Cyclone Track Forecasting Using Fused Deep Learning From Aligned Reanalysis Data.tif

The forecast of tropical cyclone trajectories is crucial for the protection of people and property. Although forecast dynamical models can provide high-precision short-term forecasts, they are computationally demanding, and current statistical forecasting models have much room for improvement given that the database of past hurricanes is constantly growing. Machine learning methods, that can capture non-linearities and complex relations, have only been scarcely tested for this application. We propose a neural network model fusing past trajectory data and reanalysis atmospheric images (wind and pressure 3D fields). We use a moving frame of reference that follows the storm center for the 24 h tracking forecast. The network is trained to estimate the longitude and latitude displacement of tropical cyclones and depressions from a large database from both hemispheres (more than 3,000 storms since 1979, sampled at a 6 h frequency). The advantage of the fused network is demonstrated and a comparison with current forecast models shows that deep learning methods could provide a valuable and complementary prediction. Moreover, our method can give a forecast for a new storm in a few seconds, which is an important asset for real-time forecasts compared to traditional forecasts.

热带气旋路径预报对于保障民众生命财产安全至关重要。尽管动力预报模型可提供高精度的短期预报，但此类模型计算量庞大；鉴于历史飓风数据库正持续扩容，当前的统计预报模型仍有较大改进空间。能够捕捉非线性关系与复杂关联的机器学习方法，在该应用场景中的测试仍较为匮乏。本研究提出一种融合历史路径数据与大气再分析图像（风场与气压三维场）的神经网络模型，针对24小时路径追踪预报，我们采用以风暴中心为基准的移动参考坐标系。该网络基于跨南北半球的大型数据库（1979年以来共3000余场风暴，每6小时采样一次），训练以预测热带气旋与热带低压的经纬度位移。本研究验证了融合模型的优势，通过与现有预报模型的对比可知，深度学习方法可提供具有价值的互补性预报结果。此外，本方法可在数秒内完成单场新风暴的预报，相较于传统预报方法，这一特性对于实时预报而言是一项重要优势。