WRF Simulations to Investigate Processes Across Scales (WRFSCALE)

Hans-Stefan Bauer (), Oliver Branch and Benjamin Körner
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Hans-Stefan Bauer: University of Hohenheim, Institute of Physics and Meteorology
Oliver Branch: University of Hohenheim, Institute of Physics and Meteorology
Benjamin Körner: University of Hohenheim, Institute of Physics and Meteorology

A chapter in High Performance Computing in Science and Engineering '23, 2026, pp 475-494 from Springer

Abstract: Abstract Several scientific aspects ranging from boundary layer research and land modification experiments to turbulence research were addressed in this project with the Weather Research and Forecast (WRF) model and the Parallelized Large Eddy Simulation Model (PALM) applying resolutions from km-scale down to meter-scale. Case study simulations in as different regions as the central United States, The United Arab Emirates and southwestern Germany were performed to investigate the evolution of the convective boundary layer. The multi-nested WRF-setup driven by the operational analysis of the European Centre for Medium-range Weather Forecasts (ECMWF), high-resolution terrain, land cover and soil data sets simulated a realistic evolution of the turbulent internal structure of the boundary layer including realistic transitions between nighttime and daytime conditions and interactions between the surface and the overlying atmosphere. Land modification simulations in the United Arab Emirates showed that plantations of 10 $$\,\times \,$$ × 10 km $$^{2}$$ 2 have only a small influence, but larger plantations clearly modify the weather patterns in a way that more precipitation reaches the desert. Monin-Obukhov Similarity Theory (MOST), based on simplyfied assumptions is commonly used for parameterizing turbulent fluxes. However, several processes are ignored that are the more important the finer the resolution is. Therefore, the implementation of MOST in the models will be improved with very high-resolution idealized PALM simulations, providing training data for neural networks.

Date: 2026
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DOI: 10.1007/978-3-031-91312-9_32

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