Snow Depth Measurements Research Articles

Insights into mountain precipitation and snowpack from a basin‐scale wireless‐sensor network

AbstractA spatially distributed wireless‐sensor network, installed across the 2154 km2 portion of the 5311 km2 American River basin above 1500 m elevation, provided spatial measurements of temperature, relative humidity, and snow depth in the Sierra Nevada, California. The network consisted of 10 sensor clusters, each with 10 measurement nodes, distributed to capture the variability in topography and vegetation cover. The sensor network captured significant spatial heterogeneity in rain versus snow precipitation for water‐year 2014, variability that was not apparent in the more limited operational data. Using daily dew‐point temperature to track temporal elevational changes in the rain‐snow transition, the amount of snow accumulation at each node was used to estimate the fraction of rain versus snow. This resulted in an underestimate of total precipitation below the 0°C dew‐point elevation, which averaged 1730 m across 10 precipitation events, indicating that measuring snow does not capture total precipitation. We suggest blending lower elevation rain gauge data with higher‐elevation sensor‐node data for each event to estimate total precipitation. Blended estimates were on average 15–30% higher than using either set of measurements alone. Using data from the current operational snow‐pillow sites gives even lower estimates of basin‐wide precipitation. Given the increasing importance of liquid precipitation in a warming climate, a strategy that blends distributed measurements of both liquid and solid precipitation will provide more accurate basin‐wide precipitation estimates, plus spatial and temporal patters of snow accumulation and melt in a basin.

Antarctic pack ice algal distribution: Floe‐scale spatial variability and predictability from physical parameters

AbstractAntarctic pack ice serves as habitat for microalgae which contribute to Southern Ocean primary production and serve as important food source for pelagic herbivores. Ice algal biomass is highly patchy and remains severely undersampled by classical methods such as spatially restricted ice coring surveys. Here we provide an unprecedented view of ice algal biomass distribution, mapped (as chlorophyll a) in a 100 m by 100 m area of a Weddell Sea pack ice floe, using under‐ice irradiance measurements taken with an instrumented remotely operated vehicle. We identified significant correlations (p < 0.001) between algal biomass and concomitant in situ surface measurements of snow depth, ice thickness, and estimated sea ice freeboard levels using a statistical model. The model's explanatory power (r2 = 0.30) indicates that these parameters alone may provide a first basis for spatial prediction of ice algal biomass, but parameterization of additional determinants is needed to inform more robust upscaling efforts.

Scale-dependent effects of post-fire canopy cover on snowpack depth in montane coniferous forests.

Winter snowpack in dry montane regions provides a valuable ecosystem service by storing water into the growing season. Wildfire in coniferous montane forests has the potential to indirectly affect snowpack accumulation and ablation (mass loss) rates by reducing canopy cover, which reduces canopy interception of snow but also increases solar radiation and wind speed. These counteracting effects create uncertainty regarding the canopy conditions that maximize post-fire snowpack duration, which is of concern as montane regions across the western United States experience increasingly warm, dry winters with below-average snowpack. The net effect of wildfire on snowpack depth and duration across the landscape is uncertain, and likely scale dependent. In this study, I tested whether intermediate levels of wildfire severity maximize snowpack depth by increasing accumulation while slowing ablation, using gridded, repeated snow depth measurements from three fires in the Sierra Nevada of California. Increasing fire severity had a strong negative effect on snowpack depth, suggesting that increased ablation after fire, rather than increased accumulation, was the dominant control over snowpack duration. Contrary to expectations, the unburned forest condition had the highest overall snowpack depth, and mean snow depth among all site visits was reduced by 78% from unburned forest to high-severity fire. However, at the individual tree scale, snowpack depth was greater under canopy openings than underneath canopy, controlling for effects of fire severity and aspect. This apparent paradox in snowpack response to fire at the stand vs. individual tree scales is likely due to greater variation in canopy cover within unburned and very low severity areas, which creates smaller areas for snow accumulation while reducing ablation via shading. Management efforts to maximize snowpack duration in montane forests should focus on retaining fine-scale heterogeneity in forest structure.

Terrestrial Remote Sensing of Snowmelt in a Diverse High-Arctic Tundra Environment Using Time-Lapse Imagery

Snow cover is one of the crucial factors influencing the plant distribution in harsh Arctic regions. In tundra environments, wind redistribution of snow leads to a very heterogeneous spatial distribution which influences growth conditions for plants. Therefore, relationships between snow cover and vegetation should be analyzed spatially. In this study, we correlate spatial data sets on tundra vegetation types with snow cover information obtained from orthorectification and classification of images collected from a time-lapse camera installed on a mountain summit. The spatial analysis was performed over an area of 0.72 km2, representing a coastal tundra environment in southern Svalbard. The three-year monitoring is supplemented by manual measurements of snow depth, which show a statistically significant relationship between snow abundance and the occurrence of some of the analyzed land cover types. The longest snow cover duration was found on “rock debris” type and the shortest on “lichen-herb-heath tundra”, resulting in melt-out time-lag of almost two weeks between this two land cover types. The snow distribution proved to be consistent over the different years with a similar melt-out pattern occurring in every analyzed season, despite changing melt-out dates related to different weather conditions. The data set of 203 high resolution processed images used in this work is available for download in the supplementary materials.

Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

New insights on permafrost genesis and conservation in talus slopes based on observations at Flüelapass, Eastern Switzerland

The talus slope at Flüelapass was the first mountain permafrost study site in Switzerland in the 1970s and the presence of ice-rich permafrost at the foot of the slope has been investigated in the context of several studies focusing on the role of snow cover distribution. We review previously developed hypotheses and present new ones using various data sources, such as temperature measurements in boreholes, a subaquatic DEM generated from unmanned aerial system (UAS) photogrammetry, terrestrial laser scan measurements of snow depth, geophysical ground investigations and automatic time-lapse photography. From this combination of data sources together with observations in the field, an interesting sequence of geomorphologic processes is established at Flüelapass. As a result we show how mass wasting processes can initiate the genesis and long-term conservation of ice-rich permafrost at the base of a talus slope.

Fully-automated estimation of snow depth in near real time with the use of unmanned aerial vehicles without utilizing ground control points

A new method for on-demand production of a numerical snow depth map, based on observations carried out in near real time by an unmanned aerial vehicle (UAV), is elaborated. The novelty of the approach resides in the neglection of artificial ground control points (GCPs). The method is based on processing oblique aerial geotagged images of snow-covered terrain taken with visible-light (RGB) camera installed on UAV. The compressed photographs (JPG format) are pre-processed with the use of parallel computing in order to resize the images. The structure-from-motion (SfM) procedure – herein based on the RunSFM solution – is utilized to generate a non-georeferenced dense point cloud. The subsequent automatic georeferencing procedure comprises two stages: (1) initial registration of the snow-covered dense point cloud using the Helmert transformation, the parameters of which are estimated from camera positions in local (image) and Universal Transverse Mercator (UTM) coordinate systems, (2) final registration of the snow-covered dense point cloud with the iterative closest point (ICP) algorithm applied in respect to a reference snow-free and highly accurate dense point cloud. The second phase is based on the automated ICP application to two subsets of these dense point clouds which include only tall land cover elements, mainly trees. This enables the ICP-based transformation of the source (with snow) dense point cloud into the reference one (snow-free). The two dense point clouds are subsequently interpolated to digital surface models (DSMs), the difference of which forms a numerical map of the estimated snow depth. The procedure is tested on the RGB images taken by the micro fixed-wing UAV in the Izerskie Mountains in southwestern Poland. In addition, a reproducibility test, which compares the above-mentioned procedure with its replica based on modified settings (NIR camera, lower flight altitude, bigger number of images, higher resolution, SfM reconstruction produced with Theia and OpenMVS), is also performed. The estimation is validated against spatially-distributed snow depth measurements. It is found that the method resolves snow depth with a correct order of magnitude. Mean absolute error (MAE) of snow depth estimation varied between 0.33 and 0.43m, whereas measured snow depths were between 0.24 and 1.06m (mean of 0.41m). Medium level of reproducibility is reported, with considerable similarity in statistics of snow depth estimation and only partial (local) agreement between spatial patterns of estimated and measured snow depth fields. In addition, the results show potential for using NIR images in producing realistic estimated snow depth surfaces.

Characterising effects of management practices, snow cover, and soil texture on soil temperature: Model development in DNDC

Agro-ecosystem models, such as the DNDC (DeNitrification and DeComposition) model are useful tools when assessing the sustainability of agricultural management. Accuracy in soil temperature estimations is important as it regulates many important soil biogeochemical processes that lead to greenhouse gas emissions (GHG). The objective of this study was to account for the effects of snow cover in terms of the measured snow depth (mm of water), soil texture and crop management in temperate latitudes in order to improve the surface soil temperature mechanism in DNDC and thereby improve GHG predictions. The estimation of soil temperature driven by the thermal conductivity and heat capacity of the soil was improved by considering the soil texture under frozen and unfrozen conditions along with the effects of crop canopy and snow depth. Calibration of the developed model mechanisms was conducted using data from Alfred, ON under two contrasting soil textures (sandy loam vs. clay). Independent validation assessments were conducted using soil temperatures at different depths for contrasting managements for two field sites located in Canada (Guelph, ON and Glenlea, MB). The validation results indicated high model accuracy (R 2 > 0.90, EF ≥ 0.90, RMSE 2 O emissions during spring thaw and provide a foundation for future studies aimed at improving simulations in DNDC for better representations of other biogeochemical processes.

Snow depth from ICESat laser altimetry — A test study in southern Norway

Direct snow depth measurements are sparse, especially in remote areas. In this study, we assess the potential of ICESat laser altimetry for providing snow depths for its operational period 2003–2009 on the example of the Scandinavian Mountains in southern Norway. Snow cover during ICESat campaigns typically results in positive elevation differences (dh) between ICESat GLAH14 elevations and reference elevations from Digital Elevation Models (DEMs). Three DEMs are used: the Norwegian national DEM for the entire study area, and the SRTM DEM and a high-resolution airborne lidar DEM for a spatial subset on the Hardangervidda mountain plateau. To account for uncertainty in elevation data, ICESat samples are grouped into spatial subsets, elevation bands, and over time (e.g. all winter campaigns together). We find that ICESat has the potential to provide regional-scale snow depths for the years 2003–2009 for its winter (March) and late spring (June) campaigns. ICESat-derived snow depth time series for different elevation bands agree well with measured (RMSE 0.47m) and modelled (RMSE 0.61m) snow depths in the study area. Annual differences in snow amounts and the increase of snow depths with elevation and coastal proximity over the study area are correctly reproduced. Uncertainties in reference elevations exceed ICESat elevation uncertainty and good control over errors and biases in reference DEMs turn out essential. Spatially varying vertical offsets between ICESat and the reference DEMs make it necessary to bias-correct March/June snow depths with autumn dh per spatial unit or elevation band. Best results are achieved when samples are summarised per season over the entire observation period. After correction of local DEM biases, the spatial pattern of ICESat 2003–2009 March dh matches spatially distributed modelled snow depths in southern Norway with decimeter-scale accuracy. In the western part of Hardangervidda, ICESat-based March snow depths agree better with measurements (RMSE ≤0.15m for all DEMs) than modelled snow depths do (RMSE 0.61m). In eastern Hardangervidda, the coarse resolution SRTM DEM (RMSE 0.41m) performs better than the 10m Norwegian DEM (RMSE 0.64m) which is based on a less consistent mosaic of elevation data. Using the high-resolution lidar DEM, even single footprints show good agreement (R2 0.59, RMSE 0.94m) with measured snow depths from the same year. Snow depth estimates could be further improved by using full waveform ICESat data or elevation measurements from ICESat-2 once this satellite is operational. Good quality reference DEMs may still be acquired in the future even in areas where no such data exists today.

Behavioral plasticity in a variable environment: snow depth and habitat interactions drive deer movement in winter

In seasonally varying environments, animals should alter habitat selection through time to avoid the harshest conditions. Winter severity is limiting for many ungulates in high-latitude ecosystems, and quality of habitat is an important determinant of winter survival. Previous studies in Southeast Alaska indicated that Sitka black-tailed deer (Odocoileus hemionus sitkensis) selected old-growth forest that provides both snow interception and forage, but with great variability among studies, years, and geographic areas. Clearcut timber harvest has greatly reduced the extent and quality of old-growth forest. The value of 2nd-growth and old-growth forest types to deer likely depends on snow depth, which is highly variable in space and time. We measured selection for vegetation classes, landscape features, and forage biomass by monitoring 56 GPS-radiocollared adult female deer from 1 January to 1 April between 2011 and 2013. Simultaneously, we measured snow depth across deer home ranges daily. We determined that snow depth had a strong effect on selection for vegetation classes. During periods of low snow, deer selected young 2nd growth but avoided old 2nd growth and high-volume old growth. As snow depths increased, young 2nd growth was avoided and deer selected old 2nd-growth and productive old-growth forests. The composition of vegetation classes within the landscape influenced selection, with deer selecting locally abundant habitats. These behaviors suggest that the widespread distribution of forest patches that provide snow interception and forage biomass may be critical to fulfilling the energetic requirements of deer during winters with snow. Such context-dependent habitat selection is likely widespread among wildlife species in variable environments and should be incorporated into study design and analysis.

Subgrid parameterization for snow depth over mountainous terrain from flat field snow depth

AbstractSnow depth is an important variable for a variety of models including land‐surface, meteorological, and climate models. Various measurement networks were therefore developed to measure snow depth on the ground. Measurement stations are generally located in gentle terrain (flat field measurements) most often at lower or mid elevation. While these sites have provided a wealth of information, various studies have questioned the representativity of such flat field measurements for the surrounding topography, especially in alpine regions. Using highly resolved snow depth maps at the peak of winter from two distinct climatic regions in Switzerland and in the Spanish Pyrenees, we developed two parameterizations to estimate domain‐averaged snow depth in coarse‐scale model applications over complex topography using easy to derive topographic parameters. The first parameterization uses a commonly applied linear lapse rate. Removing the dominant elevation gradient in mean snow depth revealed remaining underlying correlations with other topographic parameters, in particular the sky view factor. The second parameterization combines a power law elevation trend scaled with the subgrid parameterized sky view factor. Using a variety of statistic measures showed that the more complex parameterization performs better when using mean high‐resolution flat field snow depth. The performances slightly decreased when formulating the parameterizations for a single flat field snow depth measurement. Nevertheless, the more complex parameterization still outperformed the linear lapse rate model. As the parameterization was developed independently of a specific geographic region, we suggest it could be used to assimilate flat field snow depth or snowfall into coarse‐scale snow model frameworks.

Analysis of snow-vegetation interactions in the low Arctic-Subarctic transition zone (northeastern Canada)

Recent studies have shown that northern vegetation has been growing in relation to a warming climate over the last four decades, especially across the transition zone between tundra and taiga. Shrub growth affects snow properties and the surface energy budget, which must be better studied to quantify shrub-snow-climate feedbacks. The objective of this research is to improve the characterization of the impact of shrubs on snow evolution, from its accumulation to its melt, using in-situ and satellite measurements. The research is presented for the Umiujaq site, Nunavik, representative of the low Arctic–Subarctic transition zone. Snow depth, measured along numerous transects spanning different land cover types is found to increase by a factor 2.5–3 between tundra and forest, while snow density decreases. This illustrates the trapping effect of vegetation well. Complementary, continuous snow depth measurements using weather stations from two sites (tundra with low shrubs and a small clearing with shrubs within the forest) show different site-dependent behaviors. Spatial analysis from high-resolution Pleiades images combined with Landsat (Normalized Difference Snow Index) and MODIS (Fractional Snow Cover) images suggest a slight delay in melt over open and dense forest areas compared to tundra and dense high shrubs.

Snow depth does not affect recruitment in a low-density population of boreal woodland caribou (Rangifer tarandus caribou)

Winter snow depth may be an important driver of annual variability in recruitment of ungulate calves, and low calf recruitment has been implicated as a factor in declining boreal caribou (Rangifer tarandus caribou) populations. We used 11 consecutive years (2006–2016) of aerial survey data to document calf recruitment in a low-density population of boreal woodland caribou in the Northwest Territories, Canada. We measured snow depth in winter and tested two hypotheses: (1) that calf recruitment was lower in winters with greater snow depth and (2) that calf recruitment was lower following winters with greater snow depth (1-year time lag). Recruitment, the number of calves/adult female in March, ranged twofold from 0.23 to 0.45, and snow depth also ranged twofold from 41 to 85 cm. Yet, we found no support for the hypothesis that late-winter snow depth in the current or previous year was inversely related to calf recruitment.

Glaciological measurements and mass balances from Sperry Glacier, Montana, USA, years 2005–2015

Abstract. Glacier mass balance measurements help to provide an understanding of the behavior of glaciers and their response to local and regional climate. In 2005 the United States Geological Survey established a surface mass balance monitoring program on Sperry Glacier, Montana, USA. This project is the first quantitative study of mass changes of a glacier in the US northern Rocky Mountains and continues to the present. The following paper describes the methods used during the first 11 years of measurements and reports the associated results. From 2005 to 2015, Sperry Glacier had a cumulative mean mass balance loss of 4.37 m w.e. (water equivalent). The mean winter, summer, and annual glacier-wide mass balances were 2.92, −3.41, and −0.40 m w.e. yr−1 respectively. We derive these cumulative and mean results from an expansive data set of snow depth, snow density, and ablation measurements taken at selected points on the glacier. These data allow for the determination of mass balance point values and a time series of seasonal and annual glacier-wide mass balances for all 11 measurement years. We also provide measurements of glacier extent and accumulation areas for select years. All data have been submitted to the World Glacier Monitoring Service and are available at doi:10.5904/wgms-fog-2016-08. This foundational work provides valuable insight about Sperry Glacier and supplies additional data to the worldwide record of glaciers measured using the glaciological method. Future research will focus on the processes that control accumulation and ablation patterns across the glacier. Also we plan to examine the uncertainties related to our methods and eventually quantify a more robust estimate of error associated with our results.

A Romanian daily high-resolution gridded dataset of snow depth (2005-2015)

This study presents the spatial interpolation procedure from snow depth measurements at weather stations implying the following stages: (1) Spatial interpolation at 1 km × 1 km resolution of the mean multiannual values (2005-2015) corresponding to each month, computed from the data extracted from the climatological database; (2) Computation of the daily deviations against the multiannual monthly mean for every day and year over 2005-2015 and their spatial interpolation; (3) Spatio-temporal datasets were obtained through merging the two surfaces obtained in stages 1 and 2. The anomalies were considered to be the ratio between the daily snow depth values and the climatology. The spatial variability of the data used in the first stage was accounted for through the use of a series of predictors derived from the digital elevation model (DEM). To plot the maps with the climatological normals (multiannual means), the Regression-Kriging (RK) spatial interpolation method was used. In order to choose the optimum method applied in spatializing deviations, four interpolation methods were tested using a cross-validation procedure: Multiquadratic, Ordinary Kriging (separated and pooled variograms) and 3d Kriging.

A New Snow Density Parameterization for Land Data Initialization

Abstract Snow initialization is crucial for weather and seasonal prediction, but the National Centers for Environmental Prediction (NCEP) operational models have been found to produce too little snow water equivalent, partly because they assume a constant and unrealistically low snow density for the snowpack. One possible solution is to use the snow density formulation from the Noah land model used in NCEP operational forecast models. While this solution is better than the constant density assumption, the seasonal evolution of snow density in Noah is still found to be unrealistic, through the evaluation of both the offline Noah model output and the Noah snow density formulation itself. A physically based snow density parameterization is then developed, which performs considerably better than the Noah parameterization based on the measurements from the SNOTEL network over the western United States and Alaska. It also performs better than the snow density schemes used in three other models. This parameterization could be easily implemented in NCEP operational snow initialization. With the consideration of up to 10 snow layers, this parameterization can also be applied to multilayer snowpack initiation or to estimate snow water equivalent from in situ and airborne snow depth measurements.

Estimating Snow Depth and Snow Water Equivalence Using Repeat-Pass Interferometric SAR in the Northern Piedmont Region of the Tianshan Mountains

Snow depth and Snow Water Equivalence (SWE) are important parameters for hydrological applications. In this application, a theoretical method of snow depth estimation with repeat-pass InSAR measurements was proposed, and a preliminary sensitivity analysis of snow phase changes versus the incident angle and snow density was developed. Moreover, the snow density and incident angle parameters were analyzed and calibrated, and the local incident angle was used as a substitute for the satellite incident angle to improve the snow depth estimation. From the results, the coherence images showed that a high degree of coherence can be found for dry snow, and, apart from the effect of snow, land use/cover change due to a long temporal baseline and geometric distortion due to the rugged terrain were the main constraints for InSAR technique to measure snow depth and SWE in this area. The result of snow depth estimation between July 2008 and February 2009 demonstrated that the average snow depth was about 20 cm, which was consistent with the field survey results. The areal coverage of snow distribution estimated from the snow depth and SWE results was consistent with snow cover obtained from HJ-1A CCD optical data at the same time.

Investigation of Snow Cover Effects and Attenuation Correction of Gamma Ray in Aerial Radiation Monitoring

In aerial radiation monitoring (ARM), the air dose rate cannot be appropriately estimated under snowy conditions due to attenuation of gamma rays by the snow layer. A technique to address this issue is required for ARM to obtain enough signals for air dose rates. To develop this technique, we investigated the relationship between snow depth and ARM measurement results using ARM, laser imaging detection and ranging, and ground measurement before and after snowfall. From the measured data, the results obtained using three different correction factors were examined and compared. An appropriate correction improved the underestimation of the air dose rate. However, further improvement in the accuracy of the analysis requires accurate estimation of the snow water equivalent.

Numerical Weather Forecasts at Kilometer Scale in the French Alps: Evaluation and Application for Snowpack Modeling

Abstract Numerical weather prediction (NWP) systems operating at kilometer scale in mountainous terrain offer appealing prospects for forecasting the state of snowpack in support of avalanche hazard warning, water resources assessment, and flood forecasting. In this study, daily forecasts of the NWP system Applications of Research to Operations at Mesoscale (AROME) at 2.5-km grid spacing over the French Alps were considered for four consecutive winters (from 2010/11 to 2013/14). AROME forecasts were first evaluated against ground-based measurements of air temperature, humidity, wind speed, incoming radiation, and precipitation. This evaluation shows a cold bias at high altitude partially related to an underestimation of cloud cover influencing incoming radiative fluxes. AROME seasonal snowfall was also compared against output from the Système d’Analyse Fournissant des Renseignements Atmosphériques à la Neige (SAFRAN) specially developed for alpine terrain. This comparison reveals that there are regions of significant difference between the two, especially at high elevation, and possible causes for these differences are discussed. Finally, AROME forecasts and SAFRAN reanalysis have been used to drive the snowpack model Surface Externalisée (SURFEX)/Crocus (SC) and to simulate the snowpack evolution over a 2.5-km grid covering the French Alps during four winters. When evaluated at the experimental site of Col de Porte, both simulations show good agreement with measurements of snow depth and snow water equivalent. At the scale of the French Alps, AROME-SC exhibits an overall positive bias, with the largest positive bias found in the northern and central French Alps. This study constitutes the first step toward the development of a distributed snowpack forecasting system using AROME.

Ensemble mean density and its connection to other microphysical properties of falling snow as observed in Southern Finland

Abstract. In this study measurements collected during winters 2013/2014 and 2014/2015 at the University of Helsinki measurement station in Hyytiälä are used to investigate connections between ensemble mean snow density, particle fall velocity and parameters of the particle size distribution (PSD). The density of snow is derived from measurements of particle fall velocity and PSD, provided by a particle video imager, and weighing gauge measurements of precipitation rate. Validity of the retrieved density values is checked against snow depth measurements. A relation retrieved for the ensemble mean snow density and median volume diameter is in general agreement with previous studies, but it is observed to vary significantly from one winter to the other. From these observations, characteristic mass–dimensional relations of snow are retrieved. For snow rates more than 0.2 mm h−1, a correlation between the intercept parameter of normalized gamma PSD and median volume diameter was observed.