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Open AccessArticle

Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches

1
School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
2
Department of Climate and Energy Systems Engineering, Ewha Woman’s University, Seoul 03760, Korea
3
Hong Kong Observatory, 134A Nathan Road, Kowloon, Hong Kong, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(4), 631; https://doi.org/10.3390/rs10040631
Received: 13 March 2018 / Revised: 10 April 2018 / Accepted: 17 April 2018 / Published: 19 April 2018
(This article belongs to the Special Issue Remote Sensing Methods and Applications for Traffic Meteorology)
Even though deicing or airframe coating technologies continue to develop, aircraft icing is still one of the critical threats to aviation. While the detection of potential icing clouds has been conducted using geostationary satellite data in the US and Europe, there is not yet a robust model that detects potential icing areas in East Asia. In this study, we proposed machine-learning-based icing detection models using data from two geostationary satellites—the Communication, Ocean, and Meteorological Satellite (COMS) Meteorological Imager (MI) and the Himawari-8 Advanced Himawari Imager (AHI)—over Northeast Asia. Two machine learning techniques—random forest (RF) and multinomial log-linear (MLL) models—were evaluated with quality-controlled pilot reports (PIREPs) as the reference data. The machine-learning-based models were compared to the existing models through five-fold cross-validation. The RF model for COMS MI produced the best performance, resulting in a mean probability of detection (POD) of 81.8%, a mean overall accuracy (OA) of 82.1%, and mean true skill statistics (TSS) of 64.0%. One of the existing models, flight icing threat (FIT), produced relatively poor performance, providing a mean POD of 36.4%, a mean OA of 61.0, and a mean TSS of 9.7%. The Himawari-8 based models also produced performance comparable to the COMS models. However, it should be noted that very limited PIREP reference data were available especially for the Himawari-8 models, which requires further evaluation in the future with more reference data. The spatio-temporal patterns of the icing areas detected using the developed models were also visually examined using time-series satellite data. View Full-Text
Keywords: icing detection; machine learning; geostationary satellite data; COMS; Himawari-8 icing detection; machine learning; geostationary satellite data; COMS; Himawari-8
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MDPI and ACS Style

Sim, S.; Im, J.; Park, S.; Park, H.; Ahn, M.H.; Chan, P.-W. Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches. Remote Sens. 2018, 10, 631.

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