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Author

Im, Jungho
Intelligent Remote sensing and geospatial Information Systems (IRIS) Lab
Research Interests
  • Remote sensing, Geospatial modeling, Climate change

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Detection of tropical overshooting cloud tops using himawari-8 imagery

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Title
Detection of tropical overshooting cloud tops using himawari-8 imagery
Author
Kim, MiaeIm, JunghoPark, HaemiPark, SeonyoungLee, Myong-InAhn, Myoung-Hwan
Keywords
overshooting tops; Himawari-8; random forest; extremely randomized trees; logistic regression
Issue Date
201707
Publisher
MDPI AG
Citation
REMOTE SENSING, v.9, no.7, pp.685 -
Abstract
Overshooting convective cloud Top (OT)-accompanied clouds can cause severe weather conditions, such as lightning, strong winds, and heavy rainfall. The distribution and behavior of OTs can affect regional and global climate systems. In this paper, we propose a new approach for OT detection by using machine learning methods with multiple infrared images and their derived features. Himawari-8 satellite images were used as the main input data, and binary detection (OT or nonOT) with class probability was the output of the machine learning models. Three machine learning techniques-random forest (RF), extremely randomized trees (ERT), and logistic regression (LR)-were used to develop OT classification models to distinguish OT from non-OT. The hindcast validation over the Southeast Asia andWest Pacific regions showed that RF performed best, resulting in a mean probabilities of detection (POD) of 77.06% and a mean false alarm ratio (FAR) of 36.13%. Brightness temperature at 11.2 μm (Tb11) and its standard deviation (STD) in a 3 × 3 window size were identified as the most contributing variables for discriminating OT and nonOT classes. The proposed machine learning-based OT detection algorithms produced promising results comparable to or even better than the existing approaches, which are the infrared window (IRW)-texture and water vapor (WV) minus IRW brightness temperature difference (BTD) methods.
URI
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DOI
http://dx.doi.org/10.3390/rs9070685
ISSN
2072-4292
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UEE_Journal Papers
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