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Preprints
https://doi.org/10.5194/egusphere-2024-2012
https://doi.org/10.5194/egusphere-2024-2012
12 Jul 2024
 | 12 Jul 2024

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, and Christiane Voigt

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.

Competing interests: One of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics. The peer-review process will be guided by an independent editor, and the authors have no other competing interests to declare.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Journal article(s) based on this preprint

07 Mar 2025
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, and Christiane Voigt
Atmos. Chem. Phys., 25, 2845–2861, https://doi.org/10.5194/acp-25-2845-2025,https://doi.org/10.5194/acp-25-2845-2025, 2025
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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Upper tropospheric relative humidity bias in the ERA5 weather model is corrected by 9 % by an...
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