Preprints
https://doi.org/10.5194/gmd-2022-118
https://doi.org/10.5194/gmd-2022-118
Submitted as: development and technical paper
14 Jun 2022
Submitted as: development and technical paper | 14 Jun 2022
Status: a revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

Optimization of weather forecasting for cloud cover over the European domain using the meteorological component of the Ensemble for Stochastic Integration of Atmospheric Simulations version 1.0

Yen-Sen Lu1, Garrett Good2, and Hendrik Elbern3 Yen-Sen Lu et al.
  • 1Institute of Energy and Climate Research – Troposphere (IEK-8), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
  • 2Fraunhofer Institute for Energy Economics and Energy System Technology IEE, Königstor 59, 34119 Kassel, Germany
  • 3Rhenish Institute for Environmental Research at the University of Cologne, Cologne, Germany

Abstract. In this study, we present an expansive sensitivity analysis of physics configurations for cloud cover using the Weather Forecasting and Research Model (WRF V3.7.1) on the European domain. The experiments utilize the meteorological part of a large ensemble framework known as the Ensemble for Stochastic Integration of Atmospheric Simulations (ESIAS-met). The experiments first seek the best deterministic WRF physics configuration by simulating over 1,000 combinations of microphysics, cumulus parameterization, planetary boundary layer physics (PBL), surface layer physics, radiation scheme and land surface models. The results on six different test days are compared to CMSAF satellite images from EUMETSAT. We then selectively conduct stochastic simulations to assess the best choice for ensemble forecasts. The results indicate a high variability in terms of physics and parameterization. The combination of Goddard, WSM6, or CAM5.1 microphysics with MYNN3 or ACM2 PBL exhibited the best performance in Europe. For probabilistic simulations, the combination of WSM6 and SBU–YL microphysics with MYNN2 and MYNN3 showed the best performance, capturing the cloud fraction and its percentiles with 32 ensemble members. This work also demonstrates the capability and performance of ESIAS-met for large ensemble simulations and sensitivity analysis.

Yen-Sen Lu et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-118', Anonymous Referee #1, 03 Jul 2022
    • AC1: 'Reply on RC1', Yen-Sen Lu, 12 Oct 2022
  • RC2: 'Comment on gmd-2022-118', Anonymous Referee #2, 30 Aug 2022
    • AC2: 'Reply on RC2', Yen-Sen Lu, 12 Oct 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-118', Anonymous Referee #1, 03 Jul 2022
    • AC1: 'Reply on RC1', Yen-Sen Lu, 12 Oct 2022
  • RC2: 'Comment on gmd-2022-118', Anonymous Referee #2, 30 Aug 2022
    • AC2: 'Reply on RC2', Yen-Sen Lu, 12 Oct 2022

Yen-Sen Lu et al.

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Short summary
The weather forecasting and research model WRF consists of many parameters and options to adapt to different conditions. This expansive sensitivity study uses a large-scale simulation system to determine the most suitable options for predicting cloud cover in Europe for deterministic as well as probabilistic weather predictions for day-ahead forecasting simulations.