Orographic lifting of air masses and other topographically modified flows induce cloud and precipitation formation at larger scales and preferential deposition of precipitation at smaller scales. In this study, we examine orographic effects on small-scale snowfall patterns in Alpine terrain. A polarimetric X-band radar was deployed in the area of Davos (Switzerland) to determine the spatial variability of precipitation. In order to relate measured precipitation fields to flow dynamics, we model flow fields with the atmospheric prediction model "Advanced Regional Prediction System. " Additionally, we compare radar reflectivity fields with snow accumulation at the surface as modeled by Alpine3D. We investigate the small-scale precipitation dynamics for one heavy snowfall event in March 2011 at a high resolution of 75 m. The analysis of the vertical and horizontal distribution of radar reflectivity at horizontal polarization and differential reflectivity shows polarimetric signatures of orographic snowfall enhancement near the summit region. Increasing radar reflectivity at horizontal polarization over the windward slopes toward the crest and downwind decreasing reflectivity over the leeward slopes is observed. The temporal variation of the location of maximum concentration of snow particles is partly attributed to the effect of preferential deposition of snowfall: For situations with strong horizontal winds, the concentration maximum is shifted from the ridge crest toward the leeward slopes. Qualitatively, we discuss the relative role of cloud microphysics such as the seeder-feeder mechanism versus atmospheric particle transport in generating the observed snow deposition at the ground.

An X-band polarimetric radar was deployed in the eastern Swiss Alps at an altitude of 2133 m. Radar measurements were complemented with several weather stations deployed in an altitude range from 1500 to 3100 m as well as with a fixed GPS ground station that was used to infer integrated water vapor estimates. Around 8000 vertical profiles of polarimetric radar observables above the melting layer collected during two months are analyzed. First, the behavior of the mean profiles of reflectivity at horizontal polarization Zh, differential reflectivity Zdr, copolar cross correlation ρhv, and specific differential phase shift Kdp are interpreted from a microphysical point of view. It is shown that the whole evolution of snowflakes, from pristine crystals at temperatures around −30°C to dendritic crystals around −15°C, to large aggregates around 0°C, is well captured by the polarimetric radar variables. In a second step, the profiles are analyzed as functions of high and low water vapor and snow accumulation conditions. It is found that the vertical profiles of polarimetric radar variables have distinct features in low versus high water vapor conditions. High water vapor conditions appear to favor the occurrence of crystal aggregates at high altitudes/low temperatures. It is shown with a hydrometeor identification scheme that graupel-like particles are found to be dominant right above the melting layer for snow events with high accumulation intensities. The present analyses show that measurements from X-band dual-polarization radar can be useful to characterize the dominant microphysical processes during precipitation in mountainous regions.

[1] In mountainous regions, snow accumulation on the ground is crucial for mountain hydrology and water resources. The present study investigates the link between the spatial variability in snowfall and in snow accumulation in the Swiss Alps. A mobile polarimetric X-band radar deployed in the area of Davos (Switzerland) . The spatial distribution of snow accumulation exhibits a strong interannual consistency that can be generalized over the winters in the area. This unique configuration makes the comparison of the variability in total snowfall amount estimated from radar and in snow accumulation possible over the diverse winter periods. As expected, the spatial variability, quantified by means of the variogram, is shown to be larger in snow accumulation than in snowfall. However, the variability of snowfall is also significant, especially over the mountain tops, leads to preferential deposition during snowfall and needs further investigation. The higher variability at the ground is mainly caused by snow transport.

Abstract. The first hydrometeor classification technique based on two-dimensional video disdrometer (2DVD) data is presented. The method provides an estimate of the dominant hydrometeor type falling over time intervals of 60 s during precipitation, using the statistical behavior of a set of particle descriptors as input, calculated for each particle image. The employed supervised algorithm is a support vector machine (SVM), trained over 60 s precipitation time steps labeled by visual inspection. In this way, eight dominant hydrometeor classes can be discriminated. The algorithm achieved high classification performances, with median overall accuracies (Cohen's K) of 90 % (0.88), and with accuracies higher than 84 % for each hydrometeor class.

A new algorithm for the accurate estimation of the specific differential phase shift on propagation (K dp ) from noisy total differential phase shift (Ψ dp ) measurements is presented for data acquired with a polarimetric weather radar. The new approach, which is based on the compilation of ensembles of Kalman filter estimates, does not rely on additional data like the reflectivity or the differential reflectivity in order to constrain the solution, and it is based on Ψ dp only. The dependence of the solution on Ψ dp only allows one to apply the algorithm in various environmental conditions without reducing its performance. Drawbacks that are usually inherent in algorithms of this kind (like the loss of the small-scale structure and the smoothing of high peak values) are partially overcome by a two-step algorithm design, which first determines an ensemble of possible solutions and then selects and averages the ensemble members such that the estimated K dp profile has a better agreement with the truth. The algorithm is thoroughly evaluated and compared with a commonly used algorithm on stochastically simulated profiles of raindrop size distribution. It is found that the accuracy of the K dp values estimated with the new algorithm significantly increases. The algorithm is also experimentally evaluated by applying it on X-band radar data that were acquired in northern Brazil during the CHUVA campaign and at a high alpine site in Switzerland during snowfall. Results show that the spatial fine structure and the high values of precipitation are better represented with the new method.