论文标题
多丙烯量热带气旋轨道预测的双分支时空融合网络
Dual-Branched Spatio-temporal Fusion Network for Multi-horizon Tropical Cyclone Track Forecast
论文作者
论文摘要
热带气旋（TC）是一种极端的热带天气系统，可以通过各种时空数据来描述其轨迹。这些数据的有效挖掘是准确TCS跟踪预测的关键。但是，现有方法面临这样的问题：模型复杂性过高，或者很难从多模式数据中有效提取特征。在本文中，我们提出了双支线时空融合网络（DBF-NET） - 一种新型的多型型热带气旋轨道预测模型，可有效融合多模式特征。 DBF-NET包含一个TC特征分支，该分支从TCS的1D固有特征和压力场分支中提取时间特征，并从重新分析2D压力场中提取时空特征。通过基于编码器的体系结构和有效的功能融合，DBF-NET可以完全挖掘两种类型的数据的信息，并获得良好的TCS跟踪预测结果。与现有的统计和深度学习TCS轨道预测方法相比，西北太平洋地区历史TCS跟踪数据的广泛实验表明，我们的DBF-NET取得了显着改善。
Tropical cyclone (TC) is an extreme tropical weather system and its trajectory can be described by a variety of spatio-temporal data. Effective mining of these data is the key to accurate TCs track forecasting. However, existing methods face the problem that the model complexity is too high or it is difficult to efficiently extract features from multi-modal data. In this paper, we propose the Dual-Branched spatio-temporal Fusion Network (DBF-Net) -- a novel multi-horizon tropical cyclone track forecasting model which fuses the multi-modal features efficiently. DBF-Net contains a TC features branch that extracts temporal features from 1D inherent features of TCs and a pressure field branch that extracts spatio-temporal features from reanalysis 2D pressure field. Through the encoder-decoder-based architecture and efficient feature fusion, DBF-Net can fully mine the information of the two types of data, and achieve good TCs track prediction results. Extensive experiments on historical TCs track data in the Northwest Pacific show that our DBF-Net achieves significant improvement compared with existing statistical and deep learning TCs track forecast methods.
