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Knowledge about the spatial distribution of seasonal snow is essential e.g. to efficiently manage fresh water resources or for hydro-power companies. The large-scale gradient of snow accumulation over mountain ranges is mainly determined by lifting condensation and downstream drying. On the slope-scale, snow redistribution by wind and avalanches is the main source of variability. On a mountain-ridge to mountain-valley scale, small-scale orographic precipitation enhancement and preferential deposition interact and lead to asymmetric snow distribution across mountain ridges. However, their relative importance is barely known and the characteristics of preferential deposition are still under debate. Yet, especially in a changing climate, which may go along with modified dominant wind directions, it is important to understand precipitation processes shaping the snow cover. Therefore, we investigate terrain-flow-precipitation interactions and their effect on mountain-ridge to mountain-valley scale snow precipitation and deposition in complex alpine terrain. To this end, the Weather Research and Forecasting (WRF) model is set up to downscale Consortium for Small-Scale Modeling (COSMO) analysis to a horizontal resolution of 50 m using a nesting approach. At 450 m resolution these simulations fairly represent large-scale precipitation variability with respect to high-resolution operational weather radar precipitation estimates, capturing the effect of large-scale orographic enhancement. Although, the model misses substantial small-scale precipitation variability even at a 50 m resolution, we demonstrate that the lee-side flow field and mountain-ridge scale precipitation processes start to be represented at this resolution. Thus, a model resolution of at least 50 m is required to represent mountain-ridge to mountain-valley scale precipitation patterns, which is far higher than model resolutions conventionally used to simulate snow water resources in alpine regions. Small-scale orographic precipitation enhancement and mean advection are estimated to increase lee-side precipitation by up to 20%, while a conservative estimate of (near-surface) preferential deposition reveals lee-side snow deposition enhancement on the order of 10%. However, both processes strongly depend on atmospheric conditions such as atmospheric humidity or the strength of mean advection. The peculiarity of the lee-side flow field is of particular importance for the spatial distribution of snow accumulation, especially with regards to preferential deposition. This is further demonstrated by a very persistent eddy-like structure on the leeward side of the Sattelhorn ridge in the Dischma valley (Davos, CH), as reported based on Doppler wind lidar measurements and with corresponding flow field simulations at a resolution of 25 m by the Advanced Regional Prediction System (ARPS). Corresponding snow accumulation, assessed by the means of terrestrial laser scanning, confirms that snow distribution in very steep terrain is strongly influenced by post-depositional snow redistribution. Nevertheless, we can report a certain agreement of simulated pre-depositional precipitation patterns across mountain ridges with photogrammetrically determined snow distribution. Overall, we demonstrate the necessity and value of high-resolution snow precipitation measurements and simulations, and we contribute to the understanding of the small-scale variability of snow distribution in alpine terrain.

Reliable predictions of sea level rise require a quantitative understanding of the mass balance of the Antarctic ice sheet. Water vapor exchange between snow and the atmospheric boundary layer may be an important term in the mass balance equation but current estimates for this process are highly uncertain. The exchange of water vapor becomes particularly strong during drifting and blowing snow events, which are frequent in Antarctica. In these conditions, measured turbulent fluxes based on the Eddy–Covariance (EC) method or the Monin–Obukhov (MO) bulk formula are associated with increased uncertainties that are difficult to quantify. The EC raw data can contain artifacts because blowing snow particles temporarily perturb the measurement signals of ultrasonic anemometers and open–path infrared gas analyzers. The MO bulk approach suffers from the fact that sources or sinks of moisture and heat in the drifting and blowing snow layer violate the assumption of height–constant fluxes. Additionally, strong winds typically result in small vertical differences in temperature and humidity, which makes the MO bulk approach particularly sensitive to instrument–specific biases in temperature and humidity. In view of these limitations, it is difficult to validate models, especially the parametrizations used in large-scale models. Nevertheless, detailed small-scale numerical simulations can help to constrain vapor and heat exchange and to disentangle different sources of uncertainty. In this study, we use Large-Eddy Simulations (LES) as a reference to validate and improve parametrizations of latent and sensible heat fluxes in conditions of drifting and blowing snow. The boundary conditions of the LES simulations are based on field measurements at the Japanese Syowa S17 Station, coastal East Antarctica. Consistent with the almost flat and permanently snow-covered terrain, the LES simulations assume a flat snow surface a the lower boundary of the domain (approximately 38 x 19 x 18 m^3). The transport of snow particles and their interaction with the flat surface is represented by a Lagrangian Stochastic Model coupled with the LES. Vapor and heat exchange between snow particles and the air is based on the energy and mass balance of a spherical ice particle in turbulent flow, neglecting radiative heat transfer. In contrast to most other modeling studies on drifting and blowing snow, the LES simulations do not assume a thermal equilibrium with constant particle temperatures but the model explicitly computes the particle temperatures, which increases the accuracy of the vapor and heat exchange. LES-based steady-state vertical profiles of the latent and sensible heat fluxes are compared with parametrized profiles in a simple one-dimensional model, resembling the approach of existing large-scale models. In the simple model, we focus for simplicity on a suitable representation of the vapour and heat exchange between particles of drifting and blowing snow and the atmosphere, not yet the parametrization of the particle concentration. We propose the following three modifications, which improve significantly the parametrization: (i) additional model levels in the lowest few centimeters above the surface, (ii) prognostic computation of air temperature and specific humidity also at the near-surface levels, and (iii) an empirical expression for the change in particle temperature derived from the LES data. This work is an important step towards reliable parametrizations of drifting snow effects.

Spatially distributed meteorological information at the slope scale is relevant for many processes in complex terrain, yet information at this sub-km spatial resolution is difficult to obtain. While downscaling to kilometer resolutions is well described in literature, moving beyond the kilometer scale is not. In this work, we present a methodical comparison of three downscaling methods of varying complexity, that are used to downscale data from the Numerical Weather Prediction model COSMO-1 at 1.1 km horizontal resolution to 250 and 50 m over a domain of highly complex terrain in the Swiss Alps. We compare WRF, a dynamical atmospheric model; ICAR, a model of intermediate complexity; and TopoSCALE, an efficient topography-based downscaling scheme. Point-scale comparisons show similar results amongst all three models w.r.t. mean-error statistics, but underlying dynamics are different. Ridge-flow interactions show reasonable agreement between WRF and ICAR at 250 m model resolution. However, at 50 m resolution WRF is able to simulate complex flow patterns that ICAR cannot. Validation against Lidar data suggests that only WRF is able to capture preferential deposition of snow. Based on these findings and the significant reduction in computational costs, ICAR is a cost efficient alternative to WRF at the 250 m resolution. TopoScale performs very well in point-scale comparisons, but it is unclear if this can be attributed to the model itself or to the forcing data and the observations assimilated therein. Further study is required to quantify this effect.

When a well-developed, high velocity katabatic flow draining down the ice sheet of Antarctica reaches the coast, it experiences an abrupt and rapid transition due to change in slope resulting in formation of a hydraulic jump. A remarkable manifestation of the hydraulic jump, given the "right" surface conditions, is the large-scale entrainment and convergence of blowing snow particles within the hydraulic jump. This can result in formation of 100-1000 m high, highly localized "walls" of snow in the air in an otherwise cloud-free sky.Recent work by Vignon et al. (2020) has described in detail, the mechanisms resulting in the formation of hydraulic jumps and excitation of gravity waves during a particularly notable event at the Dumont d"Urville (DDU) station in August 2017. They used a combination of satellite images, mesoscale simulations with WRF and station measurements (including Micro Rain Radars) in their study, notably relying on the snow wall for diagnosing and quantifying the hydraulic jump in satellite images. On the other hand, relatively less importance was given towards the surface snow processes including the transport of snow particles in the wall.In this presentation, we present results from simulations done using the recently developed CRYOWRF v1.0 to recreate the August 2017 episode at DDU and explicitly simulate the formation and the dynamics of the snow wall itself. CRYOWRF enhances the standard WRF model with the state-of-the-art surface snow modelling scheme SNOWPACK as well as a completely new blowing snow scheme. SNOWPACK essentially acts as a land surface model for the WRF atmospheric model, thus making a quantum leap over the existing snow cover models in WRF. Since SNOWPACK is a grain-scale snow model, it allows for the proper formulation of boundary conditions for simulating blowing snow dynamics.Results show the formation of the snow wall due to large scale entrainment over a wide area of the ice sheet, the mass balance of the snow wall within the hydraulic jump and finally, the destruction of the snow wall and the ultimate fate of all the entrained snow. We also show results for the influence of the snow wall on the local surface radiation at DDU. Overall, we test the capabilities of CRYOWRF to simulate such a complex phenomenon and highlight possible applications now feasible due the tight coupling of an advanced snow cover model and a multi-scale, non-hydrostatic atmospheric flow solver.

Sublimation of snow is a major depletion mechanism, particularly in dry and windy environments such as high mountains or polar regions. Yet the quantification of the latent heat flux is a difficult task, and both measurements and estimates from models still have large uncertainties. In particular, remote areas in high mountain terrain and on the large polar ice sheets are known to have insufficient spatial coverage through measurements and insufficient capabilities of models to accurately estimate snow sublimation. Additionally, power requirements for eddy covariance (EC) systems are often beyond supply in extreme environments. We present latent heat flux measurements obtained using standard EC instrumentation from several high-alpine and Antarctic field sites and compute corresponding sublimation rates. Where possible, these quantities are compared to sublimation rates derived from measurements of meteorological variables along a vertical profile (bulk approach). This study further explores the suitability of (low-cost) alternative sensors and instrumentation to determine latent heat fluxes over snow and ice-covered surfaces. Specifically, inexpensive fast-response humidity sensors are in the focus of the approach. If successful, this may lead to denser networks of surface stations in the Alps and potentially in polar regions, capable to measure sublimation from snow. A similar approach is tested for fast-frequency air temperature measurements, investigating whether such a methodology is viable in typically stably stratified boundary layers over cold surfaces. At its early stage, this project is largely explorative but may pave the road for interesting and affordable sensing solutions for measuring turbulent heat fluxes in cold, snow and ice-covered environments as standard components on existing traditional automatic weather stations.

Snow covered regions are frequently subjected to strong winds. This leads to the erosion of the snow surface and the occurrence of drifting and blowing snow events. To correctly predict and model these phenomena is of the utmost importance to assess snowpack stability and avalanche formation, as well as airborne snow sublimation and the resultant surface mass balance. Nonetheless, snow transport is frequently neglected or misrepresented in regional and mesoscale models. One of the main challenges is the accurate representation of snow transport close to the ground, where snow particles are transported by a process called saltation. This shallow layer comprises most of the horizontal mass flux and sets the lower boundary condition to model snow suspension clouds. A detailed study of snow saltation dynamics has been conducted using a Large-Eddy-Simulation flow solver coupled with a Lagrangian model for particle trajectories. The effect of particle size distribution and interparticle cohesion on particle speed, mass flux and surface friction velocity has systematically been investigated. The results show that snow cohesion and grain size heterogeneity can significantly increase saltation mass flux, specially at high friction velocities. Moreover, the agreement between simulation results and the saltation models typically used in large scale atmospheric models is highly dependent on the assumed bed characteristics. These findings can support the development of comprehensive saltation models that specifically take into account snow bed properties. These new saltation models can be implemented in mesoscale atmospheric models and have the potential to significantly improve surface mass balance predictions if grain-scale snow surface properties are available. This is possible in the newly developed CRYOWRF model which expands WRF’s surface modeling suite by including the advanced complexity, grain-scale snow model, SNOWPACK, along with a blowing snow scheme.

The local snow distribution in complex alpine terrain is strongly influenced by small-scale terrain-flow-precipitation interaction processes such as local orographic precipitation enhancement and preferential deposition. To improve avalanche forecasting, prediction of seasonal snow water resources and flood prevention, it is important to quantitatively understand these processes. Our large-eddy configuration of the Weather Research and Forecasting model (WRF) shows that a horizontal grid spacing of ≤ 50 m is required to resolve local orographic precipitation, lee-side flow separation, and thereby preferential deposition. Two case studies in the upper Dischma valley (Davos, Switzerland) demonstrate that at this resolution precipitation patterns across mountain ridges have a strong spatial and temporal variability which can be explained by atmospheric humidity and stability conditions in agreement with theoretical concepts. Based on our case study, the overall effect of terrain-flow-precipitation interactions may increase snow accumulation on the leeward side of the mountain ridge by about 26-28% with respect to snow accumulation on the windward side. Using a simple method to distinguish preferential deposition and orographic precipitation processes, results indicate that cloud dynamics and mean advection may locally lead to a 20 % increase of precipitation on the leeward side compared to the windward side. Preferential deposition accounts for a precipitation increase of up to 10 % on the leeward side of mountain ridges. Filtered snow depth measurements show a certain agreement with the modeled precipitation distribution. In future work, a full validation will require the detailed simulation of snow transport and snow depth measurements over a larger area.

The objective of this project has been to explore the potential of the wind over complex terrain, as an energy source during wintertime in Switzerland. A wind assessment method is developed based on short-term wind profile measurements with a wind lidar, long-duration meteorological station measurements, which are connected via machine learning to a specific site. Driving a high-resolution numerical weather model (WRF) with COSMO model as an input, we create a map of the spatial pattern of the local wind speed potential for short episodes of predominating weather patterns. We further use Wind-Topo, a very recent machine learning model, which predicts wind potential at high spatial and temporal resolution, to reproduce first the WRF simulations and then yearly averages. The yearly averages are used as a basis of comparison with the Swiss wind atlas. This report provides the local air flow analysis based on two measurement campaigns at La Stadera, near the Lukmanier Pass, GR, and at Cabane, Glacier3000 near Les Diablerets, VD, as examples of 3D wind assessment in complex terrain. Furthermore, a previous assessment in Eastern Switzerland is re-calculated with Wind-Topo. The results show that the Swiss wind atlas provides a good estimate of wind potential at the two measurement sites and that spatial patterns are comparable but not identical to Wind-Topo. The in-depth analysis of spatial patterns from both, Wind-Topo and WRF, suggest that areas of high wind potential may be missed by the wind atlas in particular in slopes and valleys. The spatial analysis presented here has limited validation and we suggest further investigation of these effects and an update of the Swiss wind atlas at higher temporal and spatial resolution. This appears necessary to assist and promote the transition of the Swiss electricity supply system towards renewable energy resources.

Knowledge on changes in seasonal mountain snow water resources are essential e.g. for hydropower companies. Both, snow accumulation and ablation need to be investigated to make precise predictions of water stored in a seasonal snow cover. Only if the processes behind snow accumulation and ablation are understood with sufficient quantitative accuracy, the evolution of snow water resources under a changing climate can be addressed. It is known that different snow precipitation processes and snow redistribution are responsible for snow accumulation patterns in alpine terrain. In a small-scale analysis of radar data in the region of Davos, Mott et al. (2014) could identify different snow deposition patterns for homogeneous precipitation, seeder-feeder mechanism, preferential deposition and a combination of the seeder-feeder mechanism and preferential deposition. In addition to the snow precipitation mechanisms, snow redistribution due to snow-atmosphere interaction is essential to characterize snow accumulation patterns at small scales (Scipión et al., 2013). In this study we investigate small-scale patterns of precipitation for an extended area over the Dischma valley (Davos, CH) for the winter season 2014/2015. An X-band polarimetric radar was installed on a slope facing the Dischma valley and it conducted plane position indicator (PPI) scans at elevation angles of 7° and 10° (minimum distance to the ground is about 300m and 500m, respectively) and three range height indicator (RHI) scans along the Dischma valley and along the Landwasser valley (i.e. along Davos). These radar products are available with horizontal and vertical resolution of 75 meters and a high temporal resolution of 5 minutes. The specific spatial patterns revealed by the radar measurements allow to characterize the different types of winter precipitation as well as to identify cloud microphysical and dynamical processes that govern the precipitation distribution. The continuous radar measurements are also used to analyze the frequency of certain types of hydrometeors and precipitation genesis processes as well as snow precipitation patters, which are related to specific atmospheric situations. For a few snowfall events, we additionally analyze terrestrial laser scans (TLS) of steep rock faces with different orientations that were performed before and after the snow precipitation events. The results allow us to relate identified accumulation patterns to the identified precipitation patterns and confirm the importance of redistribution processes for accumulation in steep terrain.