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

Abstract

Persistent contrail cirrus form in Ice-Supersaturated Regions (ISSRs) and are responsible for a large portion of aviation's non-CO2 climate impact. Avoiding ISSRs through flight rerouting has been proposed as a short-term mitigation strategy. However, accurate prediction of the Relative Humidity with respect to ice, RHi, distribution within ISSRs at cruising altitude remains difficult. Observations are problematic: Satellite-based global measurements carry large uncertainties while in-situ measurements offer a limited spatial coverage. On the contrary, reanalysis offer a global estimate of RHi, but it suffers from a dry bias near the tropopause where ISSRs are located as well as significant random errors. In this study, we develop a hybrid machine learning model to improve RHi estimates in the upper troposphere and lower stratosphere using ERA5 and aircraft measurements from the In-service Aircraft for a Global Observing System. The model combines an XGBoost regressor for drier conditions (RHi < 85 %) and an Artificial Neural Network (ANN) for more humid cases (RHi > 85 %). This hybrid approach significantly outperforms raw ERA5 data, leveraging the ANN's ability to capture non-linear relationships and the XGBoost's robustness in handling drier conditions. The mean absolute error (MAE) is reduced from 13.7 % to 11.4 % and the Equitable Threat Score (ETS) for ISSR detection is improved from 0.36 to 0.44. The greatest improvement is observed in the lower stratosphere, where the ETS increases by 0.18 and the MAE drops to 10.71 %. These improvements mark a key step toward more reliable identification of ISSRs, helping reduce the uncertainties that currently limit effective flight-rerouting strategies.