This title appears in the Scientific Report :
2022
Renewable Energy Potential Analysis Based on High-Resolution Regional Atmospheric Modeling over Southern Africa
Renewable Energy Potential Analysis Based on High-Resolution Regional Atmospheric Modeling over Southern Africa
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 cou...
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Personal Name(s): | Chen, Shuying (Corresponding author) |
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Poll, Stefan / Goergen, Klaus / Heinrichs, Heidi / Hendricks-Franssen, Harrie-Jan | |
Contributing Institute: |
Agrosphäre; IBG-3 Technoökonomische Systemanalyse; IEK-3 |
Imprint: |
2022
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Conference: | NIC Symposium, Jülich (Germany), 2022-09-29 - 2022-09-30 |
Document Type: |
Abstract |
Research Program: |
Societally Feasible Transformation Pathways Effective System Transformation Pathways |
Publikationsportal JuSER |
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. |