CNN-Based Tropical Cyclone Track Forecasting from Satellite Infrared Images
Chong Wang, Qing Xu, Xiaofeng Li, Yongcun Cheng
Abstract
In this study, a deep convolutional neural network (CNN) was developed to forecast the movement direction of tropical cyclones (or typhoons) over the Northwestern Pacific basin from Himawari-8 (H-8) satellite images. 2250 infrared images which captured 97 typhoon cases between 2015 and 2018 were used to train the CNN model. By using images from Channels 13 and 15 as input into the CNN model, the mean error of the typhoon movement angle reaches up to 27.8°, which shows the great potential of deep learning in tropical cyclone track prediction.
Topics & Concepts
TyphoonTropical cycloneSatelliteConvolutional neural networkTrack (disk drive)MeteorologyTropical cyclone forecast modelRemote sensingComputer scienceArtificial intelligenceEnvironmental scienceGeologyGeographyEngineeringAerospace engineeringOperating systemTropical and Extratropical Cyclones ResearchEarthquake Detection and Analysis