[1] The inhomogeneous snow distribution found in alpine terrain is the result of wind and precipitation interacting with the (snow) surface over topography. We introduce and explain preferential deposition of precipitation as the deposition process without erosion of previously deposited snow and thus in absence of saltation. A numerical model is developed, describing the relevant processes of saltation, suspension, and preferential deposition. The model uses high-resolution wind fields calculated with a meteorological model, ARPS. The model is used to simulate a 120 h snow storm period over a steep alpine ridge, for which snow distribution measurements are available. The comparison to measurements shows that the model captures the larger-scale snow distribution patterns and predicts the total additional lee slope loading well. However, the spatial resolution of 25 m is still insufficient to capture the smaller-scale deposition features observed. The model suggests that the snow distribution on the ridge scale is primarily caused by preferential deposition and that this result is not sensitive to model parameters such as turbulent diffusivity, drift threshold, or concentration in the saltation layer.

[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).

Wind transport of snow can cause an additional snow load on leeward slopes, which often has a considerable influence on avalanche danger. For a quantitative assessment of this process, a model is proposed which calculates the snow transport over a two-dimensional mountain ridge, based on input measurements of wind speed and precipitation. Since the topography is idealized, the model is focused on the snow mass that is transported over the ridge, and no statements are made about the exact snow distribution over the slopes. Three transport modes are distinguished: snow transport in saltation, snow transport in Suspension, and preferential deposition of precipitation. Suspension is modelled with a one-dimensional diffusion equation, and for the saltation layer a newly developed model, based on the microscale physical processes, is implemented. The effect of speed-up of the wind over the ridge is included by assuming an analytical wind profile with a maximum wind speed at a few meters above the ridge Advective effects are taken into account in a parameterization of the turbulent shear stress profile. The model is compared with measurements taken at the experimental snowdrift site Gaudergrat in the Parsenn area, Switzerland, and good agreement is obtained between calculated and measured results.

To obtain a high-resolution wind field and to model snow drift over complex topography, the Swiss Federal Institute for Snow and Avalanche Research (SLF) uses the atmospheric model ARPS (Advanced Regional Prediction System, University of Oklahoma). ARPS accesses horizontally homogeneous initial fields. Until recently, this model was driven by atmospheric soundings recorded at sites far away from the actual model domain. In order to optimise the initial conditions of ARPS and to provide the option of producing real forecasts, a new downscaling method has been developed, which can also be applied to other uses. Based on the vertical structure of the atmosphere produced by the operational mesoscale forecast model aLMo (Alpine Model, MeteoSwiss), an artificial sounding is created for a specified location within the ARPS grid. Some of the techniques applied include model bias adjustment (based on aLMo verification studies), the introduction of horizontal filters, the assimilation of surface observation data and the vertical displacement of the boundary layer, motivated by the distinct topographies of the ARPS and the aLMo model. The combination of the methods differs from parameter to parameter. This paper describes details of the downscaling technique and gives an example of the application of the method.