Work

Prediction of a typhoon track using a generative adversarial network and satellite images

Year: 2019

Type: article

Abstract: Tracks of typhoons are predicted using a generative adversarial network (GAN) with satellite images as inputs. Time series of satellite images of typhoons which occurred in the Korea Peninsula in the past are used to train the neural network. The trained GAN is employed to produce a 6-hour-advance track of a typhoon for which the GAN was not trained. The predicted track image of a typhoon favorably identifies the future location of the typhoon center as well as the deformed cloud structures. Errors between predicted and real typhoon centers are measured quantitatively in kilometers. An averaged error of 95.6 km is achieved for tested 10 typhoons. Predicting sudden changes of the track in westward or northward directions is identified as a challenging task, while the prediction is significantly improved, when velocity fields are employed along with satellite images.

Source: Scientific Reports

DOI: 10.1038/s41598-019-42339-y

Authors: Mario Rüttgers, Sangseung Lee, Soohwan Jeon, Donghyun You

Institution: Pohang University of Science and Technology

Language: English

Topic: Tropical and Extratropical Cyclones Research

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,790

Funders: National Research Foundation, National Research Foundation of Korea