Articles | Volume 26, issue 7
https://doi.org/10.5194/acp-26-4771-2026
https://doi.org/10.5194/acp-26-4771-2026
Technical note
 | 
10 Apr 2026
Technical note |  | 10 Apr 2026

Technical note: Hybrid machine learning model for bias correction of UTLS relative humidity against IAGOS observations in ERA5 reanalysis

Mathieu Antonopoulos, Jérémie Juvin-Quarroz, and Olivier Boucher

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Cited articles

Alduchov, O. A. and Eskridge, R. E.: Improved Magnus Form Approximation of Saturation Vapor Pressure, J. Appl. Meteorol., 35, 601–609, https://doi.org/10.1175/1520-0450(1996)035<0601:imfaos>2.0.co;2, 1996. a
Appleman, H.: The formation of exhaust condensation trails by jet aircraft, B. Am. Meteorol. Soc., 34, 14–20, https://doi.org/10.1175/1520-0477-34.1.14, 1953. a
Borella, A., Vignon, É., Boucher, O., and Rohs, S.: An Empirical Parameterization of the Subgrid-Scale Distribution of Water Vapor in the UTLS for Atmospheric General Circulation Models, J. Geophys. Res.-Atmos., 129, https://doi.org/10.1029/2024JD040981, 2024. a
Boulanger, D., Thouret, V., and Petzold, A.: IAGOS final quality controlled Observational Data L2 – Time series, Aeris [data set], https://doi.org/10.25326/06, 2018. a
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '16, p. 785–794, ACM, https://doi.org/10.1145/2939672.2939785, 2016. a, b
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Aviation impacts climate by forming contrails that trap heat and can persist for hours at cruising altitudes. Forecasting these humid regions is difficult, as satellites lack accuracy, aircraft data are limited, and ERA5 reanalysis has random errors. This study presents a hybrid machine learning method that corrects ERA5 with aircraft data, using decision trees in dry air and neural networks in humid air. It improves relative humidity predictions, especially in the lower stratosphere.
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