Parameterizing surface wind speed over complex topography

Nora Helbig

doi:10.1002/2016JD025593

2017-01-19

DOI：10.1002/2016JD025593

原文链接

查看原文

摘要

En 中文

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.

Keyword:

LARGE-EDDY SIMULATIONS

BOUNDARY-LAYER

FLUXES

MODEL

FLOW

PARAMETRIZATION

PREDICTION

OROGRAPHY

SYSTEM

DRAG