Leveraging LSTM for rapid intensifications prediction of tropical cyclones

Yun Li, Ruixin Yang, Chaowei Yang, Manzhu Yu, Fei Hu, Yongyao Jiang

Research output: Contribution to journalConference article

1 Scopus citations

Abstract

Tropical cyclones (TCs) usually cause severe damages and destructions. TC intensity forecasting helps people prepare for the extreme weather and could save lives and properties. Rapid Intensifications (RI) of TCs are the major error sources of TC intensity forecasting. A large number of factors, such as sea surface temperature and wind shear, affect the RI processes of TCs. Quite a lot of work have been done to identify the combination of conditions most favorable to RI. In this study, deep learning method is utilized to combine conditions for RI prediction of TCs. Experiments show that the long short-term memory (LSTM) network provides the ability to leverage past conditions to predict TC rapid intensifications.

Original languageEnglish (US)
Pages (from-to)101-105
Number of pages5
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume4
Issue number4W2
DOIs
Publication statusPublished - Oct 19 2013
Event2nd International Symposium on Spatiotemporal Computing, ISSC 2017 - Cambridge, United States
Duration: Aug 7 2017Aug 9 2017

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All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences (miscellaneous)
  • Environmental Science (miscellaneous)
  • Instrumentation

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