| 23 Sep 2025
A Local Terrain Smoothing Approach for Stabilizing Microscale and High-Resolution Mesoscale Simulations: a Case Study Using FastEddy® (v3.0) and WRF (v4.6.0)
Abstract. High-resolution simulations at both mesoscale and microscale increasingly rely on detailed terrain datasets, but terrain-following coordinate models can suffer from numerical instabilities in steep-slope regions. To address this issue, terrain smoothing is typically applied in numerical weather prediction models, though conventional global smoothing unnecessarily reduces resolution across the entire domain. This study presents a localized terrain smoothing approach designed to prevent numerical instabilities while preserving terrain details. Different smoothing strategies were tested for efficiency, computational cost, and terrain preservation. The final approach applies a Gaussian filter with adaptive standard deviation within a localized 3×3 grid, with a blending factor of 0.2, and treating all the steep-slope points simultaneously. Integrated into the NCAR's FastEddy® LES and WRF mesoscale community models, this technique effectively prevents terrain-driven instabilities in high-resolution simulations over complex terrain. The proposed local filtering method helps minimizing loss of terrain detail and avoiding the need for excessively strong numerical filtering during run time to stabilize the simulations. This method is computationally efficient, easy to implement, and adaptable to other models, providing a robust solution to improve numerical stability while maintaining high-resolution terrain features.
This manuscript presents and compares several methods to smooth terrain data to be used in weather models with terrain-following coordinates. The contents of the paper are useful for modeling and the paper's structure is coherent. However, the presentation is not always clear. The manuscript therefore requires minor revisions before it can be published. The issues that need to be addressed are:
line 21: the 35 degree as threshold value needs some more context, since it is heavily relied upon throughout the paper. What mechanism causes the instability at 35 degrees, and why can some models tolerate steeper gradients?
line 32: what is a 'closest approach'?
line 38: 'thorough' - I do not disagree, but adjectives like this do not belong in scientific text.
line 71: The grid spacing, probably not the resolution, increases with height.
Section 2.1.2: I find the inner and extended FastEddy domains a bit confusing: it seems that only results from the extended domain are presented, so what's the use of the inner domain in this paper? Also, the naming implies that these are two (nested) simulations (like for WRF), which is not the case, if I understand it correctly.
Figure 1 caption: Typo (crush instead of crash).
line 139: How exactly is the terrain upscaled? Since the paper is about terrain data processing, this is an important detail.
line 149: it should be more explicitly stated what convergence entails.
Figure 6: What is the purpose of the metric relative elevation differences? A certain elevation difference caused by the smoothing will have a relative elevation difference that depends on the location in the domain. Why not present absolute elevation difference or slope difference?
Figure 7: In the slope density distribution, it is not clear what each color means.
Figure 7: All three resolutions have the same maximum wavenumber: 2 10-2 m-1, which corresponds to 50 m. Since the three panels use different resolutions, it is not clear what is meant with the wavenumber. Also, the y axes lack units.
line 265: 'the developed local smoothing method ensures numeral stability', this conclusion is a bit too strong (with one case study).