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 Citation (Scopus)

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
StatePublished - Oct 19 2013
Event2nd International Symposium on Spatiotemporal Computing, ISSC 2017 - Cambridge, United States
Duration: Aug 7 2017Aug 9 2017

Fingerprint

cyclones
tropical cyclone
prediction
predictions
forecasting
wind shear
sea surface temperature
Long short-term memory
surface wind
weather
learning
destruction
shear
damage
causes

All Science Journal Classification (ASJC) codes

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

Cite this

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title = "Leveraging LSTM for rapid intensifications prediction of tropical cyclones",
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.",
author = "Yun Li and Ruixin Yang and Chaowei Yang and Manzhu Yu and Fei Hu and Yongyao Jiang",
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Leveraging LSTM for rapid intensifications prediction of tropical cyclones. / Li, Yun; Yang, Ruixin; Yang, Chaowei; Yu, Manzhu; Hu, Fei; Jiang, Yongyao.

In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 4, No. 4W2, 19.10.2013, p. 101-105.

Research output: Contribution to journalConference article

TY - JOUR

T1 - Leveraging LSTM for rapid intensifications prediction of tropical cyclones

AU - Li, Yun

AU - Yang, Ruixin

AU - Yang, Chaowei

AU - Yu, Manzhu

AU - Hu, Fei

AU - Jiang, Yongyao

PY - 2013/10/19

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N2 - 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.

AB - 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.

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