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

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Detection of Convective Initiation Using Meteorological Imager Onboard Communication, Ocean, and Meteorological Based on Machine Learning Approaches

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Title
Detection of Convective Initiation Using Meteorological Imager Onboard Communication, Ocean, and Meteorological Based on Machine Learning Approaches
Author
Han, HyangsunLee, SanggyunIm, JunghoKim, MiaeLee, Myong-InAhn, Myoung HwanChung, Sung-Rae
Issue Date
2015-07
Publisher
MDPI AG
Citation
REMOTE SENSING, v.7, no.7, pp.9184 - 9204
Abstract
As convective clouds in Northeast Asia are accompanied by various hazards related with heavy rainfall and thunderstorms, it is very important to detect convective initiation (CI) in the region in order to mitigate damage by such hazards. In this study, a novel approach for CI detection using images from Meteorological Imager (MI), a payload of the Communication, Ocean, and Meteorological Satellite (COMS), was developed by improving the criteria of the interest fields of Rapidly Developing Cumulus Areas (RDCA) derivation algorithm, an official CI detection algorithm for Multi-functional Transport SATellite-2 (MTSAT-2), based on three machine learning approaches—decision trees (DT), random forest (RF), and support vector machines (SVM). CI was defined as clouds within a 16 × 16 km window with the first detection of lightning occurrence at the center. A total of nine interest fields derived from visible, water vapor, and two thermal infrared images of MI obtained 15-75 min before the lightning occurrence were used as input variables for CI detection. RF produced slightly higher performance (probability of detection (POD) of 75.5% and false alarm rate (FAR) of 46.2%) than DT (POD of 70.7% and FAR of 46.6%) for detection of CI caused by migrating frontal cyclones and unstable atmosphere. SVM resulted in relatively poor performance with very high FAR ~83.3%. The averaged lead times of CI detection based on the DT and RF models were 36.8 and 37.7 min, respectively. This implies that CI over Northeast Asia can be forecasted ~30-45 min in advance using COMS MI data.
URI
https://scholarworks.unist.ac.kr/handle/201301/16612
URL
http://www.mdpi.com/2072-4292/7/7/9184
DOI
10.3390/rs70709184
ISSN
2072-4292
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