Abstract
Recent computational and modeling advances have led a diverse modeling community to experiment with atmospheric boundary layer (ABL) simulations at subkilometer horizontal scales. Accurately parameterizing turbulence at these scales is a complex problem. The modeling solutions proposed to date are still in the development phase and remain largely unvalidated. This work assesses the performance of methods currently available in the Weather Research and Forecasting (WRF) model to represent ABL turbulence at a gray-zone grid spacing of 333 m. We consider three one-dimensional boundary layer parameterizations (MYNN, YSU and Shin-Hong) and coarse large-eddy simulations (LES). The reference dataset consists of five real-case simulations performed with WRF-LES nested down to 25 m. Results reveal that users should refrain from coarse LES and favor the scale-aware, Shin-Hong parameterization over traditional one-dimensional schemes. Overall, the spread in model performance is large for the cellular convection regime corresponding to the majority of our cases, with coarse LES overestimating turbulent energy across scales and YSU underestimating it and failing to reproduce its horizontal structure. Despite yielding the best results, the Shin-Hong scheme overestimates the effect of grid dependence on turbulent transport, highlighting the outstanding need for improved solutions to seamlessly parameterize turbulence across scales.
Highlights
Atmospheric modeling underpins research and operational activities in several fields, such as aviation, forestry, air quality and renewable energy
While increased model resolution has the potential to improve simulation performance, it can complicate the task of turbulence modeling and lead to inaccurate outcomes, especially at horizontal resolutions between the meso and microscales, a modeling region known as the “terra incognita” or “gray zone” [7,8]
The model performance for each approach is determined relative to the reference large-eddy simulation (LES) simulation, RLES
Summary
Atmospheric modeling underpins research and operational activities in several fields, such as aviation, forestry, air quality and renewable energy. While increased model resolution has the potential to improve simulation performance, it can complicate the task of turbulence modeling and lead to inaccurate outcomes, especially at horizontal resolutions between the meso and microscales, a modeling region known as the “terra incognita” or “gray zone” [7,8]. At these resolutions, the subgrid-scale (SGS) model lies between the fully parameterized regime, where the mesoscale approach based on horizontal homogeneity across the grid footprint is applicable and the large-eddy simulation (LES) regime, where small isotropic eddies are parameterized according to Kolmogorov’s theory.
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