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Remote Sens. 2015, 7(7), 9184-9204; doi:10.3390/rs70709184

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

1
School of Urban and Environmental Engineering, Ulsan National Institute of Science andTechnology, Ulsan 689-798, South Korea
2
Department of Atmospheric Science & Engineering, Ewha Womans University, Seoul 120-750, South Korea
3
National Meteorological Satellite Center, Korea Meteorological Administration, Jincheon 365-831, South Korea
Those authors contributed equally to this paper.
*
Author to whom correspondence should be addressed.
Academic Editors: Richard Müller and Prasad S. Thenkabail
Received: 7 April 2015 / Revised: 26 June 2015 / Accepted: 13 July 2015 / Published: 17 July 2015
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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. View Full-Text
Keywords: convective initiation (CI); communication; ocean and meteorological satellite (COMS); meteorological imager (MI); machine learning; interest fields; lead time; Northeast Asia convective initiation (CI); communication; ocean and meteorological satellite (COMS); meteorological imager (MI); machine learning; interest fields; lead time; Northeast Asia
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Han, H.; Lee, S.; Im, J.; Kim, M.; Lee, M.-I.; Ahn, M.H.; Chung, S.-R. Detection of Convective Initiation Using Meteorological Imager Onboard Communication, Ocean, and Meteorological Satellite Based on Machine Learning Approaches. Remote Sens. 2015, 7, 9184-9204.

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