Machine learning for improvement of upper-tropospheric relative humidity in ERA5 weather model data

Ziming Wang, Luca Bugliaro, Klaus Gierens, Michaela I. Hegglin, Susanne Rohs, Andreas Petzold, Stefan Kaufmann, Christiane Voigt

Atmospheric chemistry and physics · 07 March 2025 · Forschungszentrum Jülich / Jülich Supercomputing Centre, University of Reading

Paper

In one sentence. ANN corrects ERA5 upper-tropospheric relative humidity using IAGOS aircraft data, cutting MAE from 15.7% to 6.6% and improving contrail predictions.

Abstract

Abstract. Knowledge of humidity in the upper troposphere and lower stratosphere (UTLS) is of special interest due to its importance for cirrus cloud formation and its climate impact. However, the UTLS water vapor distribution in current weather models is subject to large uncertainties. Here, we develop a dynamic-based humidity correction method using artificial neural network (ANN) to improve the relative humidity over ice (RHi) in ECMWF numerical weather predictions. The model is trained with time-dependent thermodynamic and dynamical variables from ECMWF ERA5 and humidity measurements from the In-service Aircraft for a Global Observing System (IAGOS). Previous and current atmospheric variables within ±2 ERA5 pressure layers around the IAGOS flight altitude are used for ANN training. RHi, temperature and geopotential exhibit the highest impact on ANN results, while other dynamical variables are of minor importance. The ANN shows excellent performance and the predicted RHi in the UT has a mean absolute error MAE of 6.6 % and a coefficient of determination R2 of 0.93, which is significantly improved compared to ERA5 RHi (MAE of 15.7 %; R2 of 0.66). The ANN model also improves the prediction skill for all sky UT/LS and cloudy UTLS and removes the artificial peak at RHi = 100 %. The contrail predictions are in better agreement with MSG observations of ice optical thickness than the results without humidity correction for a contrail cirrus scene over the Atlantic. The ANN method can be applied to other weather models to improve humidity predictions and to support aviation and climate research applications.

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