Micrometeorological processes driving snow ablation in an Alpine catchment

Mountain snow covers typically become patchy over the course of a melting season. The snow pattern during melt is mainly governed by the end of winter snow depth distribution and the local energy balance. The objective of this study is to investigate micrometeorological processes driving snow ablation in an Alpine catchment. For this purpose we combine a meteorological model (ARPS) with a fully distributed energy balance model (Alpine3D). Turbulent fluxes above melting snow are further investigated by using data from eddy-correlation systems. We compare modelled snow ablation to measured ablation rates as obtained from a series of Terrestrial Laser Scanning campaigns covering a complete ablation season. The measured ablation rates indicate that the advection of sensible heat causes locally increased ablation rates at the upwind edges of the snow patches. The effect, however, appears to be active over rather short distances except for very strong wind conditions. Neglecting this effect, the model is able to capture the mean ablation rates for early ablation periods but strongly overestimates snow ablation once the fraction of snow coverage is below a critical value. While radiation dominates snow ablation early in the season, the turbulent flux contribution becomes important late in the season. Simulation results indicate that the air temperatures appear to overestimate the local air temperature above snow patches once the snow coverage is below a critical value. Measured turbulent fluxes support these findings by suggesting a stable internal boundary layer close to the snow surface causing a strong decrease of the sensible heat flux towards the snow cover. Thus, the existence of a stable internal boundary layer above a patchy snow cover exerts a dominant control on the timing and magnitude of snow ablation for patchy snow covers.<br/

How are turbulent sensible heat fluxes and snow melt rates affected by a changing snow cover fraction?

The complex interaction between the atmospheric boundary layer and the heterogeneous land surface is typically not resolved in numerical models approximating the turbulent heat exchange processes. In this study, we consider the effect of the land surface heterogeneity on the spatial variability of near-surface air temperature fields and on snow melt processes. For this purpose we calculated turbulent sensible heat fluxes and daily snow depth depletion rates with the physics-based surface process model Alpine3D. To account for the effect of a heterogeneous land surface (such as patchy snow covers) on the local energy balance over snow, Alpine3D is driven by twodimensional atmospheric fields of air temperature and wind velocity, generated with the non-hydrostatic atmospheric model Advanced Regional Prediction System. The atmospheric model is initialized with a set of snow distributions [snow cover fraction (SCF) and number of snow patches] and atmospheric conditions (wind velocities) for an idealized flat test site. Numerical results show that the feedback of the heterogeneity of the land surface (snow, no snow) on the near-surface variability of the atmospheric fields result in a significant increase in the mean air temperature ΔTa = 1.8 K (3.7 and 4.9 K) as the SCF is decreased from a continuous snow cover to 55% (25 and 5%). Mean air temperatures over snow heavily increase with increasing initial wind velocities and weakly increase with an increasing number of snow patches. Surface turbulent sensible heat fluxes and daily snow depth depletion rates are strongly correlated to mean air temperatures, leading to 22–40% larger daily snow depth depletion rates for patchy snow covers. Numerical results from the idealized test site are compared with a test site in complex terrain. As slope-induced atmospheric processes (such as the development of katabatic flows) modify turbulent sensible heat fluxes, the variation of the surface energy balance is larger in complex terrain than for an idealized flat test site

Roughness characterization of and turbulent boundary layer flow over flat snow surfaces

Abstract only

Laser induced fluorescence - particle tracking velocimetry (lif-ptv) measurements of water flow through snow

LIF-PTV-measurements of the micro-scale water flow through the pore space of a wet snow sample driven by either gravitational or capillary forces are presented. For the measurements, fluorescent micron-sized particles in the water are illuminated with a laser light sheet and tracked with a high-speed camera. The results show the existence of a potential preferential flow path and a loop flow in a pore space in case of a gravity driven flow. Generally, the water flow is found to be highly 3-dimensional. The average flow velocities in the pore spaces are 11.3 mm/s for the gravity driven flow and 9.6 mm/s for the upward flow driven by capillary forces. Flow acceleration and deceleration was stronger for the gravity driven flow with particle decelerations stronger than accelerations in both cases

Effect of unsteady wind on drifting snow: first investigations

Wind is not always a steady flow. It can oscillate, producing blasts. However, most of the current numerical models of drifting snow are constrained by one major assumption: forcing winds are steady and uniform. Moreover, very few studies have been done to verify this hypothesis, because of the lack of available instrumentation and measurement difficulties. Therefore, too little is known about the possible role of wind gust in drifting snow. In order to better understand the effect of unsteady winds, we have performed both experiments at the climatic wind tunnel at the CSTB (Centre Scientifique et Technique des Bâtiments) in Nantes, France, and in situ experiments on our experimental high-altitude site, at the Lac Blanc Pass. These experiments were carried out collaboratively with Cemagref (France), Météo-France, and the IFENA (Switzerland). Through the wind tunnel experiments, we found that drifting snow is in a state of permanent disequilibrium in the presence of fluctuating airflows. In addition, the in situ experiments show that the largest drifting snow episodes appear during periods of roughly constant strong wind, whereas a short but strong blast does not produce significant drifting snow.&nbsp;</p> <p style='line-height: 20px;'><b>Key words.</b> Drifting snow, blowing snow, gust, blast, acoustic senso

Elevation dependency of mountain snow depth

Elevation strongly affects quantity and distribution patterns of precipitation and snow. Positive elevation gradients were identified by many studies, usually based on data from sparse precipitation stations or snow depth measurements. We present a systematic evaluation of the elevation snow depth relationship. We analyse areal snow depth data obtained by remote sensing for seven mountain sites near to the time of the maximum seasonal snow accumulation. Snow depths were averaged to 100 m elevation bands and then related to their respective elevation level. The assessment was performed at three scales: (i) the complete data sets (10 km scale), (ii) sub-catchments (km scale) and (iii) slope transects (100 m scale). We show that most elevation-snow depth curves at all scales are characterised through a single shape. Mean snow depths increase with elevation up to a certain level where they have a distinct peak followed by a decrease at the highest elevations. We explain this typical shape with a generally positive elevation gradient of snow fall that is modified by the interaction of snow cover and topography. These processes are preferential deposition of precipitation and redistribution of snow by wind, sloughing and avalanching. Furthermore, we show that the elevation level of the peak of mean snow depth correlates with the dominant elevation level of rocks (if present)

Time-adaptive wind turbine model for an LES framework

Most large-eddy simulation studies related to wind energy have been carried out either by using a fixed pressure gradient to ensure that mean wind direction is perpendicular to the wind turbine rotor disk or by forcing the flow with a geostrophic wind and timely readjusting the turbines’ orientation. This has not allowed for the study of wind farm characteristics with a time-varying wind vector. In this paper, a new time-adaptive wind turbine model for the large-eddy simulation framework is introduced. The new algorithm enables the wind turbines to dynamically realign with the incoming wind vector and self-adjust the yaw orientation with the incoming wind vector similar to real wind turbines. The performance of the new model is tested first with a neutrally stratified atmospheric flow forced with a time-varying geostrophic wind vector. A posteriori, the new model is used to further explore the interaction between a synthetic time-changing thermal atmospheric boundary layer and an embedded wind farm. Results show that there is significant potential power to be harvested during the unstable time periods at the cost of designing wind turbines capable of adapting to the enhanced variance of these periods. Stable periods provide less power but are more constant over time with an enhanced lateral shear induced by an increased change in wind direction with height

How forward‐scattering snow and terrain change the Alpine radiation balance with application to solar panels

Rough terrain in mid- and high latitudes is often covered with highly reflective snow, giving rise to a very complex transfer of incident sunlight. In order to simplify the radiative transfer in weather and climate models, snow is generally treated as an isotropically reflecting material. We develop a new model of radiative transfer over mountainous terrain, which considers for the first time the forward scattering properties of snow. Combining ground-measured meteorological data and high resolution digital elevation models, we show that the forward scattering peak of snow leads to a strong local redistribution of incident terrain reflected radiation. In particular, the effect of multiple terrain reflections is enhanced. While local effects are large, area-wide albedo is only marginally decreased. In addition, we show that solar panels on snowy ground can clearly benefit from forward scattering, helping to maximize winter electricity production

Model simulations of the modulating effect of the snow cover in a rain-on-snow event

In October 2011, the Swiss Alps underwent a marked rain-on-snow (ROS) event when a large snowfall on 8 and 9 October was followed by intense rain on 10 October. This resulted in severe flooding in some parts of Switzerland. Model simulations were carried out for 14 meteorological stations in two affected regions of the Swiss Alps using the detailed physics-based snowpack model SNOWPACK. We also conducted an ensemble sensitivity study, in which repeated simulations for a specific station were done with meteorological forcing and rainfall from other stations. This allowed the quantification of the contribution of rainfall, snow melt and liquid water storage on generating snowpack runoff. In the simulations, the snowpack produced runoff about 4-6 h after rainfall started, and total snowpack runoff became higher than total rainfall after about 11-13 h. These values appeared to be strongly dependent on snow depth, rainfall and melt rates. Deeper snow covers had more storage potential and could absorb all rain and meltwater in the first hours, whereas the snowpack runoff from shallow snow covers reacts much more quickly. However, the simulated snowpack runoff rates exceeded the rainfall intensities in both snow depth classes. In addition to snow melt, the water released due to the reduction of liquid water storage contributed to excess snowpack runoff. This effect appears to be stronger for deeper snow covers and likely results from structural changes to the snowpack due to settling and wet snow metamorphism. These results are specifically valid for the point scale simulations performed in this study and for ROS events on relatively fresh snow