WRF Simulations to Investigate Processes Across Scales (WRFSCALE)

Hans-Stefan Bauer (), Thomas Schwitalla (), Oliver Branch () and Rohith Thundathil ()
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Hans-Stefan Bauer: Institute of Physics and Meteorology
Thomas Schwitalla: Institute of Physics and Meteorology
Oliver Branch: Institute of Physics and Meteorology
Rohith Thundathil: Institute of Physics and Meteorology

A chapter in High Performance Computing in Science and Engineering '20, 2021, pp 469-486 from Springer

Abstract: Abstract Different scientific aspects ranging from boundary layer research and air quality modeling to data assimilation applications were addressed with the Weather Research and Forecasting (WRF) model from the km-scale down to the turbulence-permitting scale. Case study simulations in as different regions as the central United States and the United Arab Emirates 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, and land cover data sets simulated a realistic evolution of the internal turbulent structure of the boundary layer including the transitions between daytime and nighttime conditions. Simulations with km-scale resolution over the United Arab Emirates revealed the performance of the WRF model compared to surface station data in this arid region. An air quality forecast system based on the WRF-Chem model was set up and its performance was tested with a resolution as fine as 50 m for Stuttgart. It demostrated good performance in representing the morning an evening rush hour peak concentrations and their reduction due to the developing daytime turbulence. Data assimilation experiments demonstrated the beneficial influence of state-of-the-art lidar measurements on the forecast performance of WRF. A further improvement was found when the more sophisticated hybrid 3DVAR-ETKF method was applied, since this method includes a more sophisticated flow-dependent model error contribution spreading the information of the observations more realistically in the domain.

Date: 2021
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DOI: 10.1007/978-3-030-80602-6_31

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