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@ARTICLE{Lu:911598,
      author       = {Lu, Yen-Sen and Good, Garrett and Elbern, Hendrik},
      title        = {{O}ptimization of weather forecasting for cloud cover over
                      the {E}uropean domain using the meteorological component of
                      the {E}nsemble for {S}tochastic {I}ntegration of
                      {A}tmospheric {S}imulations version 1.0},
      journal      = {Geoscientific model development discussions},
      issn         = {1991-9611},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernicus},
      reportid     = {FZJ-2022-04857},
      year         = {2022},
      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.},
      cin          = {IEK-8 / JSC},
      ddc          = {910},
      cid          = {I:(DE-Juel1)IEK-8-20101013 / I:(DE-Juel1)JSC-20090406},
      pnm          = {2113 - Future Weather and Extremes (POF4-211) / 5111 -
                      Domain-Specific Simulation $\&$ Data Life Cycle Labs (SDLs)
                      and Research Groups (POF4-511) / EoCoE-II - Energy Oriented
                      Center of Excellence : toward exascale for energy (824158)},
      pid          = {G:(DE-HGF)POF4-2113 / G:(DE-HGF)POF4-5111 /
                      G:(EU-Grant)824158},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.5194/gmd-2022-118},
      url          = {https://juser.fz-juelich.de/record/911598},
}