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

• Published in: Atmospheric chemistry and physics Vol. 25; no. 5; pp. 2845 - 2861
• Main Authors: Wang, Ziming, Bugliaro, Luca, Gierens, Klaus, Hegglin, Michaela I., Rohs, Susanne, Petzold, Andreas, Kaufmann, Stefan, Voigt, Christiane
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 07.03.2025; Copernicus Publications
• Subjects: Aircraft; Analysis; Artificial intelligence; Artificial neural networks; Aviation; Bias; Cirrus clouds; Climate; Climate and weather; Cloud formation; Contrails; Dynamic height; Flight altitude; Geopotential; Humidity; Humidity measurement; Ice cover; Lower stratosphere; Machine learning; METEOSAT; Neural networks; Optical thickness; Predictions; Relative humidity; Satellites; Stratosphere; Troposphere; Upper troposphere; Water vapor; Water vapor distribution; Water vapour; Weather; Weather forecasting

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 dyna...