Effect of grid anisotropy, resolution, and subgrid-scale models in pseudo-spectral Large Eddy Simulations of low-level clouds

TL;DR

This study evaluates how grid resolution, anisotropy, and subgrid-scale models influence large-eddy simulations of low-level clouds, proposing optimal configurations for accuracy and efficiency.

Contribution

It introduces a novel framework combining pseudo-spectral advection with AMD subgrid-scale model and identifies optimal grid anisotropy for cloud simulations.

Findings

AMD model with pseudo-spectral advection yields robust, accurate results across resolutions.

Recommended grid anisotropy has vertical spacing about three times finer than horizontal.

Grid anisotropy improves convergence rates and aligns with theoretical predictions.

Abstract

We investigate the effect due to grid resolution and subgrid-scale model on large-eddy simulations of low-level clouds using a novel framework that combines pseudo-spectral advection with the anisotropic minimum dissipation (AMD) subgrid-scale model. We use two field campaigns as reference, DYCOMS-II RF01 and ASTEX, which cover both non-precipitating and precipitating stratocumulus cloud regimes across different time scales. Our results demonstrate that the AMD model combined with pseudo-spectral advection produces robust and accurate predictions across varying grid resolutions without parameter tuning. We identify a recommended grid anisotropy where vertical spacing is approximately three times finer than horizontal spacing, balancing accuracy and computational efficiency. Finally, an error analysis based on cloud liquid water content and vertical velocity variance reveals good agreement with theoretical predictions for isotropic grids, while grid anisotropy effectively improves convergence rates.