Parameterizing surface wind speed over complex topography

• Published in: Journal of geophysical research. Atmospheres Vol. 122; no. 2; pp. 651 - 667
• Main Authors: Helbig, N., Mott, R., Herwijnen, A., Winstral, A., Jonas, T.
• Format: Journal Article
• Language: English
• Published: Washington Blackwell Publishing Ltd 27-01-2017
• Subjects: ARPS; Atmospheric models; Banks (topography); Climatology; coarse‐scale wind speed; Computer simulation; Correlation; Fields; Fields (mathematics); Frameworks; Gaussian random fields; Geographical distribution; Geophysics; High resolution; Length; Mathematical models; Mean square values; Parameterization; Parameters; Parametrization; Physiographic features; Scale (ratio); Sky; sky view factor; Slope; Slopes; Spatial distribution; statistical downscaling; Statistical methods; Statistics; subgrid parameterization; Surface wind; Terrain; Topography; Topography (geology); Weather; Weather forecasting; Wind fields; Wind speed; Switzerland
• ISSN: 2169-897X; 2169-8996
• DOI: 10.1002/2016JD025593

Subgrid parameterizations are used in coarse‐scale meteorological and land surface models to account for the impact of unresolved topography on wind speed. While various parameterizations have been suggested, these were generally validated on a limited number of measurements in specific geographical areas. We used high‐resolution wind fields to investigate which terrain parameters most affect near‐surface wind speed over complex topography under neutral conditions. Wind fields were simulated using the Advanced Regional Prediction System (ARPS) on Gaussian random fields as model topographies to cover a wide range of terrain characteristics. We computed coarse‐scale wind speed, i.e., a spatial average over the large grid cell accounting for influence of unresolved topography, using a previously suggested subgrid parameterization for the sky view factor. We only require correlation length of subgrid topographic features and mean square slope in the coarse grid cell. Computed coarse‐scale wind speed compared well with domain‐averaged ARPS wind speed. To further statistically downscale coarse‐scale wind speed, we use local, fine‐scale topographic parameters, namely, the Laplacian of terrain elevations and mean square slope. Both parameters showed large correlations with fine‐scale ARPS wind speed. Comparing downscaled numerical weather prediction wind speed with measurements from a large number of stations throughout Switzerland resulted in overall improved correlations and distribution statistics. Since we used a large number of model topographies to derive the subgrid parameterization and the downscaling framework, both are not scale dependent nor bound to a specific geographic region. Both can readily be implemented since they are based on easy to derive terrain parameters.
Key Points
Subgrid parameterization for coarse‐scale wind speed using a subgrid parameterization for the sky view factor
Statistical downscaling using fine‐scale terrain parameters and the subgrid parameterization for coarse‐scale wind speed
Validation of downscaled wind speed with measurements showed overall improved performance compared to applying coarse‐scale wind speed