Icing detection over East Asia from geostationary satellite data using machine learning approaches

Seongmun Sim, Jungho Im, Sumin Park, Haemi Park, Myoung Hwan Ahn, Pak wai Chan

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

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.

Original languageEnglish
Article number631
JournalRemote Sensing
Volume10
Issue number4
DOIs
StatePublished - 1 Apr 2018

Keywords

  • COMS
  • Geostationary satellite data
  • Himawari-8
  • Icing detection
  • Machine learning

Fingerprint

Dive into the research topics of 'Icing detection over East Asia from geostationary satellite data using machine learning approaches'. Together they form a unique fingerprint.

Cite this