scholarly journals Snow Depth and Snow Density at Resolute, Northwest Territories

1960 ◽  
Vol 3 (28) ◽  
pp. 733-738 ◽  
Author(s):  
Richmond W. Longley
Keyword(s):  
Snow Cover ◽  
Water Content ◽  
Northwest Territories ◽  
Snow Depth ◽  
Research Council ◽  
National Research Council ◽  
Snow Surface ◽  
Snow Density ◽  
Strong Winds

Abstract Observations of the snow depth at 21 sites at Resolute were made twice weekly during the winter of 1957–58. As a result of these observations, and of other observations on snow made for the National Research Council, it is shown that the snow depth and the water content of the snow did not continue to increase during the winter as the snow fell. Rather the strong winds eroded the snow surface and the increase in depth was irregular and relatively slow. Furthermore, the observations on the density of the snow cover lead to the conclusion that attempts to measure the density in similar regions with an accuracy greater than ± 0.05 g. cm.−3 are not warranted.

scholarly journals Snow Depth and Snow Density at Resolute, Northwest Territories

1960 ◽  
Vol 3 (28) ◽  
pp. 733-738
Author(s):  
Richmond W. Longley
Keyword(s):  
Snow Cover ◽  
Water Content ◽  
Northwest Territories ◽  
Snow Depth ◽  
Research Council ◽  
National Research Council ◽  
Snow Surface ◽  
Snow Density ◽  
Strong Winds

AbstractObservations of the snow depth at 21 sites at Resolute were made twice weekly during the winter of 1957–58. As a result of these observations, and of other observations on snow made for the National Research Council, it is shown that the snow depth and the water content of the snow did not continue to increase during the winter as the snow fell. Rather the strong winds eroded the snow surface and the increase in depth was irregular and relatively slow. Furthermore, the observations on the density of the snow cover lead to the conclusion that attempts to measure the density in similar regions with an accuracy greater than ± 0.05 g. cm.−3 are not warranted.

scholarly journals A one-dimensional model of the evolution of snow-cover characteristics

1993 ◽  
Vol 18 ◽  
pp. 22-26 ◽  
Author(s):  
Takeshi Yamazaki ◽  
Junsei Kondo ◽  
Takashi Sakuraoka ◽  
Toru Nakamura
Keyword(s):  
Diurnal Variation ◽  
Snow Cover ◽  
Water Content ◽  
Liquid Water ◽  
Snow Depth ◽  
Meteorological Data ◽  
Dimensional Model ◽  
Snow Density ◽  
One Dimensional ◽  
Snow Temperature

A one-dimensional model has been developed to simulate the evolution of snow-cover characteristics using meteorological data. This model takes into account the heat balance at the snow surface and heat conduction in the snow cover as well as liquid water flow and densification. The basic variables of the model are snow temperature, liquid water content, snow density and the solid impurities density. With these four variables, the model can calculate albedo, thermal conductivity, liquid water flux, snow depth, water equivalent and the amount of runoff.Diurnal variation of profiles of snow temperature, water content and snow density, and meteorological elements were observed at Mount Zao Bodaira, Yamagata Prefecture, Japan. Simulated diurnal variation patterns of each component by the model were in good agreement with the observations. Moreover, the snow-cover characteristics were simulated for three 90-day periods with meteorological data and snow pit observations at Sapporo. It was found that the model was able to simulate long-period variations of albedo, snow depth, snow water equivalent and the snow density profile.

scholarly journals A one-dimensional model of the evolution of snow-cover characteristics

1993 ◽  
Vol 18 ◽  
pp. 22-26 ◽  
Author(s):  
Takeshi Yamazaki ◽  
Junsei Kondo ◽  
Takashi Sakuraoka ◽  
Toru Nakamura
Keyword(s):  
Diurnal Variation ◽  
Snow Cover ◽  
Water Content ◽  
Liquid Water ◽  
Snow Depth ◽  
Meteorological Data ◽  
Dimensional Model ◽  
Snow Density ◽  
One Dimensional ◽  
Snow Temperature

A one-dimensional model has been developed to simulate the evolution of snow-cover characteristics using meteorological data. This model takes into account the heat balance at the snow surface and heat conduction in the snow cover as well as liquid water flow and densification. The basic variables of the model are snow temperature, liquid water content, snow density and the solid impurities density. With these four variables, the model can calculate albedo, thermal conductivity, liquid water flux, snow depth, water equivalent and the amount of runoff. Diurnal variation of profiles of snow temperature, water content and snow density, and meteorological elements were observed at Mount Zao Bodaira, Yamagata Prefecture, Japan. Simulated diurnal variation patterns of each component by the model were in good agreement with the observations. Moreover, the snow-cover characteristics were simulated for three 90-day periods with meteorological data and snow pit observations at Sapporo. It was found that the model was able to simulate long-period variations of albedo, snow depth, snow water equivalent and the snow density profile.

Measurement of snow cover depth using 100 × 100 meters sampling plot and Structure from Motion method in Adventdalen, Svalbard

2016 ◽  
Vol 6 (2) ◽  
pp. 155-168
Author(s):  
Radim Stuchlík ◽  
Jan Russnák ◽  
Tomáš Plojhar ◽  
Zdeněk Stachoň
Keyword(s):  
Spatial Distribution ◽  
Snow Cover ◽  
Structure From Motion ◽  
Snow Depth ◽  
Surface Model ◽  
Snow Surface ◽  
Reference Volume ◽  
Snow Cover Depth ◽  
Sampling Plot ◽  
Motion Method

We tried to verify the concept of Structure from Motion method for measuring the volume of snow cover in a grid of 100×100 m located in Adventdalen, Central Svalbard. As referencing method we utilized 121 depth measurements in one hectare area. Using avalanche probe a snow depth was measured in mentioned 121 nodes of the grid. We detected maximum snow depth of 2.05 m but snowless parts as well. From gathered depths’ data we geostatistically (ordinary kriging) interpolated snow surface model which we used to determine reference volume of snow at research plot (5 569 m3). As a result, we were able to calculate important metrics and analyze topography and spatial distribution of snow cover at the plot. For taking photos for Structure from Motion method, bare pole in hands with a camera mounted was used. We constructed orthomosaic of research plot.

Measurement inter-comparison of bulk snow density and water equivalent of snow cover with snow core samplers

2020 ◽  
Author(s):  
Leena Leppänen ◽  
Juan Ignazio Lopez-Moreno ◽  
Bartłomiej Luks ◽  
Ladislav Holko ◽  
Ghislain Picard ◽  
...  
Keyword(s):  
Snow Cover ◽  
Bulk Density ◽  
Snow Depth ◽  
Ground Truth ◽  
Natural Variability ◽  
Repeated Measurements ◽  
Snow Density ◽  
Field Campaign ◽  
Uncertainty Sources ◽  
Instrumental Bias

<p>Manually collected snow data can be considered as ground truth for many applications, such as climatological or hydrological studies. Water equivalent of snow cover (SWE) can be manually measured by using a snow tube or snow cylinder to extract a snow core and measure the bulk density of the core by weighing it. Different snow core samplers and scales are used, but they all use the same measurement principle. However, there are various sources of uncertainty that have not been quantified in detail. To increase the understanding of these errors, different manual SWE measurement devices used across Europe were evaluated within the framework of the COST Action ES1404 HarmoSnow. Two field campaigns were organized in different environments to quantify uncertainties when measuring snow depth, snow bulk density and SWE with core samplers. The 1<sup>st</sup> field campaign in 2017 in Iceland focused on measurement differences attributed to different instrumentation compared with the natural variability in the snowpack, and the 2<sup>nd</sup> field campaign in 2018 in Finland focused on device comparison and on the separation of the different sources of variability. To our knowledge, such a comparison has not previously been conducted in terms of the number of device and different environments.</p><p>During the 1<sup>st</sup> campaign, repeated measurements were taken along two 20 m long snow trenches to distinguish snow variability measured at the plot and at the point scale. The results revealed a much higher variability of SWE at the plot scale, resulting from both natural variability and instrument bias, compared to repeated measurements at the same spot, resulting mostly from error induced by observers or a high variability in the snow depth. Snow Micro Pen sampling showed that the snowpack was very homogeneous for the 2<sup>nd</sup> campaign, which allowed for the disregarding of the natural variability of the snowpack properties and the focus to be on separating between instrumental bias and error induced by observers. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even when observers performed measurements with snow core samplers they were not formally trained on. Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. The results showed that the devices provided slightly different uncertainties since they were designed for different snow conditions. The aim of this comparison was not to provide a definitive estimation of uncertainty for manual SWE measurements, but to illustrate the role of the different uncertainty sources.</p>

scholarly journals Snow loads on sloping roofs: two pilot studies in the Ottawa area

1985 ◽  
Vol 12 (2) ◽  
pp. 334-343 ◽  
Author(s):  
D. A. Taylor
Keyword(s):  
Surface Roughness ◽  
Research Council ◽  
National Research Council ◽  
Snow Accumulation ◽  
Snow Density ◽  
Pilot Studies ◽  
Pilot Survey ◽  
Snow Loads ◽  
On Farm ◽  
Reduction Relation

The results of two pilot studies are presented: one concerning snow on farm roofs in the Ottawa area carried out in 1966, and the other an eight-winter investigation of the influence of surface roughness and slope on snow accumulation on nine 2.4 m × 2.4 m unheated, north-facing experimental roofs built in a sheltered woods at the National Research Council Canada in Ottawa. The results indicate a trend towards reduced snow density as slopes increase and a smaller accumulation of snow on smooth (metal) surfaces than on rough shingled roofs as slopes increase. More data on full-sized roofs across Canada are required to verify this. It is suggested that a less conservative slope-reduction relation might be considered for smooth roofs in the Ottawa area and for other areas with similar climate. Key words: snow loads, sliding snow, sloping roofs, snow depths, snow densities, surface roughness, pilot survey.

scholarly journals Snow cover variability across glaciers in Nordenskiöldland (Svalbard) from point measurements in 2014–2016

2019 ◽  
Author(s):  
Marco Möller ◽  
Rebecca Möller
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
East Coast ◽  
Data Repository ◽  
Snow Density ◽  
Snow Layer ◽  
Density Measurements ◽  
Sampling Locations ◽  
Snow Water ◽  
Terrain Elevation

Abstract. Snow depths and bulk densities of the annual snow layer were measured at 69 different locations on glaciers across Nordenskiöldland, Svalbard, during the spring seasons of the period 2014–2016. Sampling locations lie along nine transects extending over 17 individual glaciers. Several of the locations were visited repeatedly, leading to a total of 109 point measurements, on which we report in this study. Snow water equivalents were calculated for each point measurement. In the dataset, snow depth and density measurements are accompanied by appropriate uncertainties which are rigorously transferred to the calculated snow water equivalents using a straightforward Monte Carlo simulation-style procedure. The final dataset can be downloaded from the Pangaea data repository (https://www.pangaea.de; https://doi.org/10.1594/PANGAEA.896581). Snow cover data indicate a general and statistically significant increase of snow depths and water equivalents with terrain elevation. A significant increase of both quantities with decreasing distance towards the east coast of Nordenskiöldland is also evident, but shows distinct interannual variability. Snow density does not show any characteristic spatial pattern.

scholarly journals Review article: Parameterizations of snow-related physical processes in land surface models

2021 ◽  
Author(s):  
Won Young Lee ◽  
Hyeon-Ju Gim ◽  
Seon Ki Park
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Land Surface Model ◽  
Surface Model ◽  
Snow Density ◽  
Physical Processes ◽  
Snow Albedo ◽  
Surface Models ◽  
Noah Land Surface Model

Abstract. Snow on land surface plays a vital role in the interaction between land and atmosphere in the state-of-the-art land surface models (LSMs) and the real world. Since the snow cover affects the snow albedo and the ground and soil heat fluxes, it is crucial to detect snow cover changes accurately. It is challenging to acquire observation data for snow cover, snow albedo, and snow depth; thus, an excellent alternative is to use the simulation data produced by the LSMs that calculate the snow-related physical processes. The LSMs show significant differences in the complexities of the snow parameterizations in terms of variables and processes considered. Thus, the synthetic intercomparisons of the snow physics in the LSMs will help the improvement of each LSM. This study revealed and discussed the differences in the parameterizations among LSMs related to snow cover fraction, snow albedo, and snow density. We selected the most popular and well-documented LSMs embedded in the Earth System Model or operational forecasting systems. We examined single layer schemes, including the Unified Noah Land Surface Model (Noah LSM), the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL), the Biosphere-Atmosphere Transfer Scheme (BATS), the Canadian Land Surface Scheme (CLASS), and multilayer schemes of intermediate complexity including the Community Noah Land Surface Model with Multi-Parameterization Options (Noah-MP), the Community Land Model version 5 (CLM 5), the Joint UK Land Environment Simulator (JULES), and the Interaction Soil-Biosphere-Atmosphere (ISBA). First, we identified that BATS, Noah-MP, JULES, and ISBA reflect the snow depth and roughness length to parameterize snow cover fraction, and CLM 5 accounts for the standard deviation of the elevation value for the snow cover decay function. Second, CLM 5 and BATS are relatively complex, so that they explicitly take into account the solar zenith angle, black carbon, mineral dust, organic carbon, and ice grain size for the determinations of snow albedo. Besides, JULES and ISBA are also complicated model which concerns ice grain size, solar zenith angle, new snow depth, fresh snowfall rate, and surface temperature for the albedo scheme. Third, HTESSEL, CLM 5, and ISBA considered the effects of both wind and temperature in the determinations of the new snow density. Especially, ISBA and JULES considered internal snow characteristics such as snow viscosity, snow temperature, and vertical stress for parameterizing new snow density. The future outlook discussed geomorphic and vegetation-related variables for the further improvement of the LSMs. Previous studies clearly show that spatio-temporal variation of snow is due to the influence of altitude, slope, and vegetation condition. Therefore, we recommended applying geomorphic and vegetation factors such as elevation, slope, time-varying roughness length, vegetation indexes, or optimized parameters according to the land surface type to parameterize snow-related physical processes.

scholarly journals SAFRAN-Crocus snow simulations in an unstable and windy climate

2001 ◽  
Vol 32 ◽  
pp. 339-344 ◽  
Author(s):  
Svanbjörg Helga Haraldsdóttir ◽  
Haraldur Ólafsson ◽  
Yves Durand ◽  
Laurent Mérindol ◽  
Gérald Giraud
Keyword(s):  
Snow Depth ◽  
Threshold Temperature ◽  
Weather Station ◽  
Main Study ◽  
Snow Density ◽  
Research Department ◽  
Weather Parameters ◽  
Strong Winds ◽  
Simulation Systems

AbstractSAFRAN and Crocus are simulation systems developed at the Centre d’Etudes de la Neige, the snow research department of Météo-France. SAFRAN analyzes weather parameters relevant to snow on the ground, and Crocus simulates the build-up of the snowpack. In this study simulated snow depths and measured snow depths at three locations in Iceland were compared. The main study was performed for the Hveravellir weather station located 640 m a.s.l. in central Iceland. The results from three winters, 1994–97, were analyzed. In Iceland the “Alpine” version of the models systematically underestimated the snow depth and density of the snowpack. Corrections for the effect of wind on snow density and on precipitation measurements led to significant improvements in simulated snow depths. The simulations are sensitive to the threshold temperature between snow and rain. The remaining problems in simulating the snowpack are mainly related to transport of snow by wind, which is not accounted for in the models, and to some extent to melting and sublimation in strong winds.

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