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 strategic flight rerouting has been proposed as a short-term mitigation strategy. However, accurate forecast of ISSRs is hindered by the difficulty of predicting Relative Humidity with respect to Ice, RH_i, at cruising altitude. Observations are problematic: Satellite-based global measurements carry large uncertainties while aircraft in-situ measurements offer a limited spatial coverage. On the contrary, ERA5 reanalysis data are offer a global estimate of RH_i, but it is known to exhibit a dry bias near the tropopause where ISSRs are located as well as significant random errors.

In this study, we develop a hybrid ensemble machine learning (ML) model to improve RH_i estimates in the Upper Troposphere (UT) and Lower Stratosphere (LS) using ERA5 and aircraft measurements from the In-service Aircraft for a Global Observing System (IAGOS). The model combines a XGBoost regressor for drier conditions (RH_i < 85 %) and an Artificial Neural Network (ANN) for more humid cases (RH_i > 85 %). This hybrid approach significantly outperforms raw ERA5 data, reducing the mean absolute error from 13.7 % to 11.4 % and improving the Equitable Threat Score (ETS) for ISSR detection from 0.36 to 0.44. The greatest improvement is observed in the lower stratosphere, where the ETS increases by 0.18 and the Mean Absolute Error (MAE) drops from 13.19 % 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.