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.

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

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2024-2012', Kevin McCloskey, 12 Jul 2024
  • CC2: 'Comment on egusphere-2024-2012', Scott Geraedts, 15 Jul 2024
  • RC1: 'Comment on egusphere-2024-2012', Anonymous Referee #1, 23 Aug 2024
    • AC1: 'Reply on RC1', Ziming Wang, 18 Nov 2024
  • CC3: 'Comment on egusphere-2024-2012', Olivier Boucher, 02 Sep 2024
  • CC4: 'Comment on egusphere-2024-2012', Olivier Boucher, 02 Sep 2024
  • RC2: 'Review of Wang et al.', Anonymous Referee #2, 10 Oct 2024
    • AC2: 'Reply on RC2', Ziming Wang, 18 Nov 2024
  • AC3: 'Reply to the community comments', Ziming Wang, 18 Nov 2024
Ziming Wang, Luca Bugliaro, Klaus Gierens, Michaela I. Hegglin, Susanne Rohs, Andreas Petzold, Stefan Kaufmann, and Christiane Voigt
Ziming Wang, Luca Bugliaro, Klaus Gierens, Michaela I. Hegglin, Susanne Rohs, Andreas Petzold, Stefan Kaufmann, and Christiane Voigt

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Short summary
Upper tropospheric relative humidity bias in the ERA5 weather model is corrected by 9 % 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 forecast and measures for contrail reduction.