  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.
