Articles | Volume 18, issue 23
https://doi.org/10.5194/amt-18-7129-2025
https://doi.org/10.5194/amt-18-7129-2025
Research article
 | 
01 Dec 2025
Research article |  | 01 Dec 2025

A novel machine learning retrieval for the detection of ice crystal icing conditions based on geostationary satellite imagery

Matteo Aricò, Dennis Piontek, Luca Bugliaro, Johanna Mayer, Richard Müller, Frank Kalinka, and Max Butter

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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-740,https://doi.org/10.5194/essd-2025-740, 2026
Preprint under review for ESSD
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Cited articles

Ayra, E. S., Rodríguez Sanz, Á., Arnaldo Valdés, R., Gómez Comendador, F., and Cano, J.: Detection and warning of ice crystals clogging pitot probes from total air temperature anomalies, Aerospace Science and Technology, 102, 105874, https://doi.org/10.1016/j.ast.2020.105874, 2020. a
Bedka, K., Yost, C., Nguyen, L., Strapp, J. W., Ratvasky, T., Khlopenkov, K., Scarino, B., Bhatt, R., Spangenberg, D., and Palikonda, R.: Analysis and automated detection of ice crystal icing conditions using geostationary satellite datasets and in situ ice water content measurements, SAE International Journal of Advances and Current Practices in Mobility, 2, 35–57, https://doi.org/10.4271/2019-01-1953, 2020. a
Bravin, M., Strapp, J. W., and Mason, J.: An investigation into location and convective lifecycle trends in an ice crystal icing engine event database, in: SAE Technical Paper Series, SAE International400 Commonwealth Drive, Warrendale, PA, US, https://doi.org/10.4271/2015-01-2130, 2015. a, b, c, d, e, f
Bugliaro, L., Zinner, T., Keil, C., Mayer, B., Hollmann, R., Reuter, M., and Thomas, W.: Validation of cloud property retrievals with simulated satellite radiances: a case study for SEVIRI, Atmos. Chem. Phys., 11, 5603–5624, https://doi.org/10.5194/acp-11-5603-2011, 2011. a
Chawla, N. V., Japkowicz, N., and Kotcz, A.: Special issue on learning from imbalanced data sets, ACM SIGKDD Explorations Newsletter, 6, 1–6, 2004. a, b, c, d
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Short summary
The goal is to assess the feasibility of an ice crystal icing detection algorithm based exclusively on remote sensing data. Active measurements are used to train and validate a newly developed random forest algorithm that is applied to passive satellite imagery to estimate the ice crystal icing conditions probability. 83 % of ice crystal icing conditions are correctly detected, showing potential for an operational implementation to mitigate its negative effects on the fleet.
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