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@INPROCEEDINGS{Chen:910819,
      author       = {Chen, Shuying and Poll, Stefan and Goergen, Klaus and
                      Heinrichs, Heidi and Hendricks-Franssen, Harrie-Jan},
      title        = {{R}enewable {E}nergy {P}otential {A}nalysis {B}ased on
                      {H}igh-{R}esolution {R}egional {A}tmospheric {M}odeling over
                      {S}outhern {A}frica},
      reportid     = {FZJ-2022-04169},
      year         = {2022},
      abstract     = {Renewable Energy Potential Analysis Based on
                      High-Resolution Regional Atmospheric Modeling over Southern
                      AfricaS. Chen, S. Poll, K. Goergen, H. Heinrichs, H.-J.
                      Hendricks-Franssen A large part of the global population
                      without reliable access to electricity lives in Africa.
                      Here, renewable energy could be a sustainable, cost
                      efficient, and climate-friendly solution, especially given
                      the large unexplored wind and solar energy potentials across
                      the African continent. Reliable and highly resolved
                      information is needed to assess renewable energy sources
                      adequately. Most often weather data like MERRA2 or ERA5 are
                      used for the assessment of renewable energy sources,
                      sometimes combined with a simple spatial downscaling based
                      on the Global Wind or Solar Atlas neglecting surface and
                      vertical atmospheric physical laws. However, those
                      meteorological input datasets typically have a relatively
                      coarse spatial resolution (e.g., ERA5 reanalysis at about
                      30km). With the aim to provide more robust data at high
                      spatial resolution, we produce a prototypical
                      high-resolution dataset over southern Africa from dedicated
                      atmospheric simulations. Such results can serve in future
                      research studies to estimate renewable energy potentials
                      with a higher spatial precision compared to previous
                      studies. As a basis for our study, we use the ICOsahedral
                      Nonhydrostatic (ICON) Numerical Weather Prediction
                      (ICON-NWP) model in its Limited Area Mode (ICON-LAM), based
                      on a configuration used also by the German Weather Service
                      (DWD) for operational weather forecasting. The study domain
                      over southern Africa is chosen due to its known favorable
                      meteorological conditions for solar and wind energy.
                      ICON-LAM dynamically downscales the global deterministic
                      ICON-NWP forecasts dataset from a grid spacing of 13km to a
                      convection-permitting resolution of 3.3km, where deep
                      convection parameterization is switched off. The
                      high-resolution triangulated grid cells of the 3.3km domain
                      are exactly inscribed in the 13km global grid cells. This
                      southern Africa ICON-LAM implementation is novel and has not
                      been run before. Simulations cover the time span from 2017
                      to 2019 with contrasting meteorological conditions. To keep
                      the ICON-LAM close to the observed atmospheric state, which
                      is assimilated into the driving global ICON-NWP runs, the
                      model atmosphere is reinitialized every 5 days, with a
                      preceding spin-up of one day. The land surface and
                      subsurface are run transient. The simulated 10m wind speed,
                      surface solar irradiance, 2m air temperature, and
                      precipitation are validated by using satellite data,
                      composite products, and in situ observations from three
                      networks (SASSCAL, TAHMO, and NCEI). This is done both for
                      the coarser driving model, the ERA5 reanalysis as well as
                      our ICON-LAM setup. Here we show initial results pointing to
                      reliable ICON-LAM simulations. Spatio-temporal Mean Bais
                      (MB) of 10m wind speed is 1.24 m s-1 and $84\%$ of simulated
                      frequency distributions overlap more than $60\%$ area with
                      that of observations. Correlation coefficients (R) of
                      surface solar irradiance have been well captured with an
                      average value over 0.9, and the spatial mean MB is 22.84 W
                      m-1. Low bias of 2m air temperature exists with a spatial
                      mean MB of 0.28 ֯C. The precipitation bias increases from
                      West to East, which may relate to the prevailing
                      precipitation regimes. All the simulations were run on the
                      cluster partition of supercomputer JUWELS at the Jülich
                      supercomputer center. The whole simulation costs 219
                      re-initialization cycles, for each cycle, 15 nodes and three
                      wall clock hours have been allocated.},
      month         = {Sep},
      date          = {2022-09-29},
      organization  = {NIC Symposium, Jülich (Germany), 29
                       Sep 2022 - 30 Sep 2022},
      cin          = {IEK-3 / IBG-3},
      cid          = {I:(DE-Juel1)IEK-3-20101013 / I:(DE-Juel1)IBG-3-20101118},
      pnm          = {1111 - Effective System Transformation Pathways (POF4-111)
                      / 1112 - Societally Feasible Transformation Pathways
                      (POF4-111)},
      pid          = {G:(DE-HGF)POF4-1111 / G:(DE-HGF)POF4-1112},
      typ          = {PUB:(DE-HGF)1},
      url          = {https://juser.fz-juelich.de/record/910819},
}