Articles | Volume 25, issue 5
https://doi.org/10.5194/acp-25-2845-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-2845-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Mar 2025
Research article |
| 07 Mar 2025
| 07 Mar 2025
Machine learning for improvement of upper-tropospheric relative humidity in ERA5 weather model data
Institute of Atmospheric Physics, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, 82234, Germany
Institute for Atmospheric Physics, Johannes Gutenberg University Mainz, Mainz, 55128, Germany
Luca Bugliaro
Institute of Atmospheric Physics, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, 82234, Germany
Klaus Gierens
Institute of Atmospheric Physics, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, 82234, Germany
Michaela I. Hegglin
Institute of Climate and Energy Systems 4 – Stratosphere (ICE-4), Forschungszentrum Jülich, Jülich, 52428, Germany
Department of Meteorology, University of Reading, Reading, RG6 6ET, UK
Susanne Rohs
Institute of Climate and Energy Systems 3 – Troposphere (ICE-3), Forschungszentrum Jülich, Jülich, 52428, Germany
Andreas Petzold
Institute of Climate and Energy Systems 3 – Troposphere (ICE-3), Forschungszentrum Jülich, Jülich, 52428, Germany
Stefan Kaufmann
Institute of Atmospheric Physics, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, 82234, Germany
Christiane Voigt
Institute of Atmospheric Physics, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, 82234, Germany
Institute for Atmospheric Physics, Johannes Gutenberg University Mainz, Mainz, 55128, Germany
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Cited
13 citations as recorded by crossref.
- Combining LIDAR, all-sky camera, and ECMWF-ERA5 reanalysis to investigate contrail formation and evolution over Clermont-Ferrand, France on June 2, 2023 S. Diarra et al. https://doi.org/10.1016/j.atmosres.2025.108500
- Technical note: Hybrid machine learning model for bias correction of UTLS relative humidity against IAGOS observations in ERA5 reanalysis M. Antonopoulos et al. https://doi.org/10.5194/acp-26-4771-2026
- The ice supersaturation biases limiting contrail modelling are structured around extratropical depressions O. Driver et al. https://doi.org/10.5194/acp-25-16411-2025
- Reduced contrail radiative effect for fleets with low soot and water vapour emissions M. Rubin-Zuzic et al. https://doi.org/10.1016/j.aeaoa.2025.100353
- Most long-lived contrails form within cirrus clouds with uncertain climate impact A. Petzold et al. https://doi.org/10.1038/s41467-025-65532-2
- Observing long-lived longwave contrail forcing A. Sonabend-W et al. https://doi.org/10.5194/amt-19-1951-2026
- Predicting ice supersaturation for contrail avoidance: ensemble forecasting using ICON with two-moment ice microphysics M. Hanst et al. https://doi.org/10.5194/acp-25-17253-2025
- Investigating the limiting aircraft-design-dependent and environmental factors of persistent contrail formation L. Megill & V. Grewe https://doi.org/10.5194/acp-25-4131-2025
- Machine Learning in Climate Downscaling: A Critical Review of Methodologies, Physical Consistency, and Operational Applications H. Najafi et al. https://doi.org/10.3390/w18020271
- An adaptive segmentation approach for contrail detection in meteosat second generation satellite imagery V. Santos Gabriel et al. https://doi.org/10.5194/amt-19-3271-2026
- Substantial aircraft contrail formation at low soot emission levels C. Voigt et al. https://doi.org/10.1038/s41586-026-10286-0
- On the Weather Impact of Contrails: New Insights from Coupled ICON–CoCiP Simulations U. Schumann & A. Seifert https://doi.org/10.5194/acp-25-18571-2025
- Variability of ice supersaturated regions at flight altitudes: evaluation of ERA5 reanalysis using IAGOS in situ measurements K. Hildebrandt et al. https://doi.org/10.5194/acp-26-6449-2026
13 citations as recorded by crossref.
- Combining LIDAR, all-sky camera, and ECMWF-ERA5 reanalysis to investigate contrail formation and evolution over Clermont-Ferrand, France on June 2, 2023 S. Diarra et al. https://doi.org/10.1016/j.atmosres.2025.108500
- Technical note: Hybrid machine learning model for bias correction of UTLS relative humidity against IAGOS observations in ERA5 reanalysis M. Antonopoulos et al. https://doi.org/10.5194/acp-26-4771-2026
- The ice supersaturation biases limiting contrail modelling are structured around extratropical depressions O. Driver et al. https://doi.org/10.5194/acp-25-16411-2025
- Reduced contrail radiative effect for fleets with low soot and water vapour emissions M. Rubin-Zuzic et al. https://doi.org/10.1016/j.aeaoa.2025.100353
- Most long-lived contrails form within cirrus clouds with uncertain climate impact A. Petzold et al. https://doi.org/10.1038/s41467-025-65532-2
- Observing long-lived longwave contrail forcing A. Sonabend-W et al. https://doi.org/10.5194/amt-19-1951-2026
- Predicting ice supersaturation for contrail avoidance: ensemble forecasting using ICON with two-moment ice microphysics M. Hanst et al. https://doi.org/10.5194/acp-25-17253-2025
- Investigating the limiting aircraft-design-dependent and environmental factors of persistent contrail formation L. Megill & V. Grewe https://doi.org/10.5194/acp-25-4131-2025
- Machine Learning in Climate Downscaling: A Critical Review of Methodologies, Physical Consistency, and Operational Applications H. Najafi et al. https://doi.org/10.3390/w18020271
- An adaptive segmentation approach for contrail detection in meteosat second generation satellite imagery V. Santos Gabriel et al. https://doi.org/10.5194/amt-19-3271-2026
- Substantial aircraft contrail formation at low soot emission levels C. Voigt et al. https://doi.org/10.1038/s41586-026-10286-0
- On the Weather Impact of Contrails: New Insights from Coupled ICON–CoCiP Simulations U. Schumann & A. Seifert https://doi.org/10.5194/acp-25-18571-2025
- Variability of ice supersaturated regions at flight altitudes: evaluation of ERA5 reanalysis using IAGOS in situ measurements K. Hildebrandt et al. https://doi.org/10.5194/acp-26-6449-2026
Saved (final revised paper)
Latest update: 11 Jun 2026
Short summary
Upper-tropospheric relative humidity bias in the ERA5 weather model is corrected by 10 % by an artificial neural network using aircraft in-service humidity data and thermodynamic and dynamical variables. The improved skills of the weather model will advance cirrus research, weather forecasts, and measures for contrail reduction.
Upper-tropospheric relative humidity bias in the ERA5 weather model is corrected by 10 % by an...
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