Dual-Branched Spatio-temporal Fusion Network for Multi-horizon Tropical Cyclone Track Forecast

TL;DR

This paper introduces DBF-Net, a novel multi-horizon tropical cyclone track forecasting model that effectively fuses multi-modal spatio-temporal data, significantly improving prediction accuracy over existing methods.

Contribution

The paper proposes a dual-branched neural network architecture that efficiently combines temporal and spatio-temporal features for improved cyclone track forecasting.

Findings

DBF-Net outperforms existing statistical and deep learning models in track prediction accuracy.

The dual-branch architecture effectively captures diverse features from multi-modal data.

Extensive experiments validate the model's superior performance on Northwest Pacific cyclone data.

Abstract

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.