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

被引：123

作者：

Ruttgers, Mario ^{[1
]}

Lee, Sangseung ^{[1
]}

Jeon, Soohwan ^{[1
]}

You, Donghyun ^{[1
]}

机构：

[1] Pohang Univ Sci & Technol, Dept Mech Engn, Pohang 37673, South Korea

来源：

SCIENTIFIC REPORTS | 2019年 / 9卷 / 1期

基金：

新加坡国家研究基金会;

关键词：

D O I：

10.1038/s41598-019-42339-y

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：15