Next Article in Journal
A New Retracking Algorithm for Retrieving Sea Ice Freeboard from CryoSat-2 Radar Altimeter Data during Winter–Spring Transition
Next Article in Special Issue
An Efficient In-Situ Debris Flow Monitoring System over a Wireless Accelerometer Network
Previous Article in Journal
Spatiotemporal Mapping and Monitoring of Whiting in the Semi-Enclosed Gulf Using Moderate Resolution Imaging Spectroradiometer (MODIS) Time Series Images and a Generic Ensemble Tree-Based Model
Previous Article in Special Issue
Time-Series Evolution Patterns of Land Subsidence in the Eastern Beijing Plain, China
Open AccessFeature PaperArticle

Machine Learning Approaches for Detecting Tropical Cyclone Formation Using Satellite Data

1
School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
2
Satellite Analysis Division, National Meteorological Satellite Center, Jincheon-gun, Chungcheongbuk-do 27803, Korea
3
Korea Ocean Satellite Center, Korea Institute of Ocean Science and Technology, Busan 49111, Korea
4
Satellite Operation and Application Center, Korea Aerospace Research Institute, Daejeon 34133, Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(10), 1195; https://doi.org/10.3390/rs11101195
Received: 15 April 2019 / Revised: 10 May 2019 / Accepted: 17 May 2019 / Published: 20 May 2019
(This article belongs to the Special Issue Advances in Remote Sensing-based Disaster Monitoring and Assessment)
This study compared detection skill for tropical cyclone (TC) formation using models based on three different machine learning (ML) algorithms-decision trees (DT), random forest (RF), and support vector machines (SVM)-and a model based on Linear Discriminant Analysis (LDA). Eight predictors were derived from WindSat satellite measurements of ocean surface wind and precipitation over the western North Pacific for 2005–2009. All of the ML approaches performed better with significantly higher hit rates ranging from 94 to 96% compared with LDA performance (~77%), although false alarm rate by MLs is slightly higher (21–28%) than that by LDA (~13%). Besides, MLs could detect TC formation at the time as early as 26–30 h before the first time diagnosed as tropical depression by the JTWC best track, which was also 5 to 9 h earlier than that by LDA. The skill differences across MLs were relatively smaller than difference between MLs and LDA. Large yearly variation in forecast lead time was common in all models due to the limitation in sampling from orbiting satellite. This study highlights that ML approaches provide an improved skill for detecting TC formation compared with conventional linear approaches. View Full-Text
Keywords: tropical cyclone formation; WindSat; machine learning tropical cyclone formation; WindSat; machine learning
Show Figures

Graphical abstract

MDPI and ACS Style

Kim, M.; Park, M.-S.; Im, J.; Park, S.; Lee, M.-I. Machine Learning Approaches for Detecting Tropical Cyclone Formation Using Satellite Data. Remote Sens. 2019, 11, 1195.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop