{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T14:32:21Z","timestamp":1779373941993,"version":"3.53.1"},"reference-count":68,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,19]],"date-time":"2018-04-19T00:00:00Z","timestamp":1524096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2014the Communication, Ocean, and Meteorological Satellite (COMS) Meteorological Imager (MI) and the Himawari-8 Advanced Himawari Imager (AHI)\u2014over Northeast Asia. Two machine learning techniques\u2014random forest (RF) and multinomial log-linear (MLL) models\u2014were 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.<\/jats:p>","DOI":"10.3390\/rs10040631","type":"journal-article","created":{"date-parts":[[2018,4,20]],"date-time":"2018-04-20T04:24:21Z","timestamp":1524198261000},"page":"631","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5426-1511","authenticated-orcid":false,"given":"Seongmun","family":"Sim","sequence":"first","affiliation":[{"name":"School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4506-6877","authenticated-orcid":false,"given":"Jungho","family":"Im","sequence":"additional","affiliation":[{"name":"School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sumin","family":"Park","sequence":"additional","affiliation":[{"name":"School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4289-0713","authenticated-orcid":false,"given":"Haemi","family":"Park","sequence":"additional","affiliation":[{"name":"School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2044-5336","authenticated-orcid":false,"given":"Myoung","family":"Ahn","sequence":"additional","affiliation":[{"name":"Department of Climate and Energy Systems Engineering, Ewha Woman\u2019s University, Seoul 03760, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pak-wai","family":"Chan","sequence":"additional","affiliation":[{"name":"Hong Kong Observatory, 134A Nathan Road, Kowloon, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,19]]},"reference":[{"key":"ref_1","unstructured":"Shappell, S., Hackworth, C., Holcomb, K., Lanicci, J., Bazargan, M., Baron, J., Iden, R., and Halperin, D. 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