Fine‐scale modeling of the boundary layer wind field over steep topography

Abstract

[1] This paper describes the adaptation of wind fields to steep and complex terrain using fine-scale numerical modeling. The work is motivated by the need of high-resolution flow fields to predict snow transport and snow cover development for avalanche warning purposes. Applying the nonhydrostatic and compressible atmospheric prediction model Advanced Regional Prediction System (ARPS) to steep alpine topography, the boundary layer flow was simulated and evaluated against measurements. The adaptation of the wind field to steep terrain for specific initial and boundary conditions was simulated. The topography used in our study has a length scale of 500 m, a typical height of 150 m, and maximum slopes of 45°. Numerical experiments with idealized triangular ridges were conducted to find an adequate model configuration. This analysis indicates that a highresolution grid with horizontal spacing of at least 25 m and vertical spacing of 3 m near the surface is necessary to reproduce small-scale flow features such as speed-up, separation, and recirculation. The onset of flow separation is highly sensitive to initial and boundary conditions, slope angle, and surface roughness. The results of the comparison between the model simulations and measurements on our experimental site show that typical wind field characteristics are well reproduced. The simulated wind fields have been used to drive a numerical three-dimensional snow drift model, which is presented in a companion paper by Lehning et al. (2008).

Inhomogeneous precipitation distribution and snow transport in steep terrain

“…Figures 1 and 2). In addition to the snow depth, meteorological parameters have been measured at 5 masts equipped with three-dimensional wind probes and standard meteorological sensors [Raderschall et al, 2008].…”

“…We only use the mean flow characteristics of the wind field for the application described here and parameterize turbulence in our drift model from similarity theory. As discussed by Raderschall et al [2008], we consider the flow field to represent the mean flow features after a short flow integration of 30 s. For longer integration times, turbulent structures develop and an averaging operation over a long time period would be required to recover the mean flow. For practical reasons, we have therefore chosen to regard the initial adaptation of the flow to the topography as a good approximation of the mean flow.…”

“…We further assume a precipitation rate of 2 mm h À1 , which leads to c 0 = 1.010 À3 kg m À3 . On the basis of our measurements of wind speed in the lee and the luff of the Gaudergrat ridge [Raderschall et al, 2008] we can assume friction velocities of 0.25 m s À1 and 0.05 m s À1 for luff and lee, respectively. According to equation (10) for luff and lee respectively.…”

“…Novel is the use of a mesoscale atmospheric model with a resolution down to 25 m for strong wind conditions. The flow field simulations are discussed in detail in a companion paper [Raderschall et al, 2008]. We only use the mean flow characteristics of the wind field for the application described here and parameterize turbulence in our drift model from similarity theory.…”

“…Alpine3D has previously been used for snow hydrology [Lehning et al, 2006], without the drifting snow module. The grid for solving the diffusion equation (3) is currently chosen identical to the ARPS grid [Raderschall et al, 2008]. Because of the terrain following coordinates in ARPS, the first grid level at the ridge is much closer to the surface than a grid level away from the ridge.…”