Leveraging LSTM for rapid intensifications prediction of tropical cyclones

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

doi:10.5194/isprs-annals-IV-4-W2-101-2017

Leveraging LSTM for rapid intensifications prediction of tropical cyclones

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

Geography

Research output: Contribution to journal › Conference article › peer-review

15 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 language	English (US)
Pages (from-to)	101-105
Number of pages	5
Journal	ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume	4
Issue number	4W2
DOIs	https://doi.org/10.5194/isprs-annals-IV-4-W2-101-2017
State	Published - Oct 19 2013
Event	2nd International Symposium on Spatiotemporal Computing, ISSC 2017 - Cambridge, United States Duration: Aug 7 2017 → Aug 9 2017

All Science Journal Classification (ASJC) codes

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

Access to Document

10.5194/isprs-annals-IV-4-W2-101-2017

Cite this

@article{c762dd92e7db41eca3c174b8888e416d,

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",

note = "Publisher Copyright: {\textcopyright} Authors 2017.; 2nd International Symposium on Spatiotemporal Computing, ISSC 2017 ; Conference date: 07-08-2017 Through 09-08-2017",

year = "2013",

month = oct,

day = "19",

doi = "10.5194/isprs-annals-IV-4-W2-101-2017",

language = "English (US)",

volume = "4",

pages = "101--105",

journal = "ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences",

issn = "2194-9042",

publisher = "Copernicus GmbH",

number = "4W2",

}

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

N1 - Publisher Copyright: © Authors 2017.

PY - 2013/10/19

Y1 - 2013/10/19

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.

UR - http://www.scopus.com/inward/record.url?scp=85048909292&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85048909292&partnerID=8YFLogxK

U2 - 10.5194/isprs-annals-IV-4-W2-101-2017

DO - 10.5194/isprs-annals-IV-4-W2-101-2017

M3 - Conference article

AN - SCOPUS:85048909292

SN - 2194-9042

VL - 4

SP - 101

EP - 105

JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

IS - 4W2

T2 - 2nd International Symposium on Spatiotemporal Computing, ISSC 2017

Y2 - 7 August 2017 through 9 August 2017

ER -

Leveraging LSTM for rapid intensifications prediction of tropical cyclones

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this