Convection‐Permitting ICON‐LAM Simulations for Renewable Energy Potential Estimates Over Southern Africa.

Renewable energy is recognized in Africa as a means for climate change mitigation, but also to provide access to electricity in sub‐Saharan Africa, where three‐quarters of the global population without electricity resides. Reliable and highly resolved renewable energy potential (REP) information is indispensable to support power plants expansion. Existing atmospheric data sets over Africa that are used for REP estimates are often characterized by data gaps, or coarse resolution. With the aim to overcome these challenges, the ICOsahedral Nonhydrostatic (ICON) Numerical Weather Prediction (ICON‐NWP) model in its Limited Area Mode (ICON‐LAM) is implemented and run over southern Africa in a hindcast dynamical downscaling setup at a convection‐permitting 3.3 km horizontal resolution. The simulation time span covers contrasting solar and wind weather years from 2017 to 2019. To assess the suitability of the novel simulations for REP estimates, the simulated hourly 10 m wind speed (sfcWind) and hourly surface solar irradiance (rsds) are extensively evaluated against a large compilation of in situ observations, satellite, and composite data products. ICON‐LAM reproduces the spatial patterns, temporal evolution, the variability, and absolute values of sfcWind sufficiently well, albeit with a slight overestimation and a mean bias (mean error (ME)) of 1.12 m s−1 over land. Likewise the simulated rsds with an ME of 50 W m−2 well resembles the observations. This new ICON simulation data product will be the basis for ensuing REP estimates that will be compared with existing lower resolution data sets. Plain Language Summary: There are still approximately 580 million people in Africa without reliable electricity supply. Renewable energy is broadly accepted as an important solution for Africa to fill the power supply gap and to mitigate climate change. Renewable energy potential (REP) information is thereby imperative for the expansion of renewable energy power planning. With conventional REP estimates, challenges are often linked to the meteorological input data, due to either the relatively coarse spatial resolution, data gaps in space and time, or data quality in general. In this study, we implemented and ran the atmospheric model ICOsahedral Nonhydrostatic (ICON) from the German Weather Service and partners at 3 km high‐resolution. The renewable energy variables wind speed and solar irradiance from these simulations are evaluated against an extensive in situ observations data set, as well as satellite, and other composite data products. In a comparison with more than 200 stations from three different in situ observation networks, it can be shown that ICON can reproduce REP‐related variables with a level of sophistication that the data is likely to offer added value over conventional inputs to REP assessments. The study is an example on how numerical models can fill in gaps in data‐scarce regions to produce useable information. Key Points: A new convection‐permitting regional ICOsahedral Nonhydrostatic (ICON) model setup over southern Africa is presented and evaluatedThe spatially and temporally highly resolved ICON wind and solar fields are inputs for improved renewable energy potential estimatesICON outputs agree well with hourly in situ observations and satellite data; wind speeds are slightly overestimated [ABSTRACT FROM AUTHOR]