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The Tibet Plateau (TP) and the Arctic are typically cold regions with abundant snow cover, which plays a key role in land surface processes. Knowledge of variations in snow density is essential for understanding hydrology, ecology, and snow cover feedback. Here, we utilized extensive measurements recorded by 697 ground-based snow sites during 1950–2019 to identify the spatio-temporal characteristics of snow density in these two regions. We examined the spatial heterogeneity of snow density for different snow classes, which are from a global seasonal snow cover classification system, with each class determined from air temperature, precipitation, and wind speed climatologies. We also investigated possible mechanisms driving observed snow density differences. The long-term mean snow density in the Arctic was 1.6 times that of the TP. Slight differences were noted in the monthly TP snow densities, with values ranging from 122 ± 29 to 158 ± 52 kg/m3. In the Arctic, however, a clear increasing trend was shown from October to June, particularly with a rate of 30.3 kg/m3 per month from March to June. For the same snow class, the average snow density in the Arctic was higher than that in the TP. The Arctic was characterized mainly by a longer snowfall duration and deeper snow cover, with some areas showing perennial snow cover. In contrast, the TP was dominated by seasonal snow cover that was shallower and warmer, with less (more) snowfall in winter (spring). The results will be helpful for future simulations of snow cover changes and land interactions at high latitudes and altitudes.

Snow, as a fundamental reservoir of freshwater, is a crucial natural resource. Specifically, knowledge of snow density spatial and temporal variability could improve modelling of snow water equivalent, which is relevant for managing freshwater resources in context of ongoing climate change. The possibility of estimating snow density from remote sensing has great potential, considering the availability of satellite data and their ability to generate efficient monitoring systems from space. In this study, we present an innovative method that combines meteorological parameters, satellite data and field snow measurements to estimate thermal inertia of snow and snow density at a catchment scale. Thermal inertia represents the responsiveness of a material to variations in temperature and depends on the thermal conductivity, density and specific heat of the medium. By exploiting Landsat 8 data and meteorological modelling, we generated multitemporal thermal inertia maps in mountainous catchments in the Western European Alps (Aosta Valley, Italy), from incoming shortwave radiation, surface temperature and snow albedo. Thermal inertia was then used to develop an empirical regression model to infer snow density, demonstrating the possibility of mapping snow density from optical and thermal observations from space. The model allows for estimation of snow density with R2CV and RMSECV of 0.59 and 82 kg m−3, respectively. Thermal inertia and snow density maps are presented in terms of the evolution of snow cover throughout the hydrological season and in terms of their spatial variability in complex topography. This study could be considered a first attempt at using thermal inertia toward improved monitoring of the cryosphere. Limitations of and improvements to the proposed methods are also discussed. This study may also help in defining the scientific requirements for new satellite missions targeting the cryosphere. We believe that a new class of Earth Observation missions with t

The Tibet Plateau (TP) and the Arctic are typically cold regions with abundant snow cover, which plays a key role in land surface processes. Knowledge of variations in snow density is essential for understanding hydrology, ecology, and snow cover feedback. Here, we utilized extensive measurements recorded by 697 ground-based snow sites during 1950–2019 to identify the spatio-temporal characteristics of snow density in these two regions. We examined the spatial heterogeneity of snow density for different snow classes, which are from a global seasonal snow cover classification system, with each class determined from air temperature, precipitation, and wind speed climatologies. We also investigated possible mechanisms driving observed snow density differences. The long-term mean snow density in the Arctic was 1.6 times that of the TP. Slight differences were noted in the monthly TP snow densities, with values ranging from 122 ± 29 to 158 ± 52 kg/m3. In the Arctic, however, a clear increasing trend was shown from October to June, particularly with a rate of 30.3 kg/m3 per month from March to June. For the same snow class, the average snow density in the Arctic was higher than that in the TP. The Arctic was characterized mainly by a longer snowfall duration and deeper snow cover, with some areas showing perennial snow cover. In contrast, the TP was dominated by seasonal snow cover that was shallower and warmer, with less (more) snowfall in winter (spring). The results will be helpful for future simulations of snow cover changes and land interactions at high latitudes and altitudes.

The aim of this project was to examine if a machine learning model could be used to predict snow density from six different weather parameters. These were artificially generated snow density, air temperature, ground temperature, relative humidity, windspeed and the snow depth change. The questions asked were what parameters correlates to the snow density, what model will perform best and could this approach be a better alternative to measure snow density manually. The research was performed in the application Regression Learner in MATLAB by testing five different premade machine learning models on a dataset. The premade models were, Linear Regression, GPR Matern 5/2, SVM Medium Gaussian, Wide Neural Network and Trilayered Neural Network. Also, the project includes data collection, data cleaning, data modification, data generation, training, testing, and evaluating the models. The results show that air temperature and windspeed overall are the most important parameters and the GPR Matern 5/2 and the Wide Neural Network had the highest performance. Lastly, it was concluded that the machine learning model could be a better alternative to measuring snow density with a real sensor., Målet med detta arbete var att undersöka om en maskininlärningsmodell kunde användas för att förutse snödensitet utifrån sex olika väderparametrar. Dessa var artificiell genererad snödensitet, lufttemperatur, marktemperatur, relativ luftfuktighet, vindhastighet och snödjupsförändring. Frågeställningarna som skulle besvaras var vilka väderparametrar som korrelerar med snödensiteten, vilken eller vilka modeller som presterade bäst samt om maskininlärningsmodellen skulle kunna vara att bättre alternativ till att mäta snödensitet manuellt. Undersökningen utfördes i applikationen Regression Learner i MATLAB genom att testa fem olika förhandsgjorda modeller vilka var Linear Regression, GPR Matern 5/2, SVM Medium Gaussian, Wide neural network och Trilayered neural network. Projektet inkluderar även datainsamling, städning av data, datamodifiering, datagenerering, träning, testning och evaluering av modellerna. Resultaten visar att lufttemperaturen och vindhastigheten över lag är viktigast för modellerna och att GPR Matern 5/2 samt Wide neural network presterade bäst. Slutligen kunde man argumentera för att maskininlärningsmodellen är ett bättre alternativ till att mäta snödensitet manuellt.

The aim of this project was to examine if a machine learning model could be used to predict snow density from six different weather parameters. These were artificially generated snow density, air temperature, ground temperature, relative humidity, windspeed and the snow depth change. The questions asked were what parameters correlates to the snow density, what model will perform best and could this approach be a better alternative to measure snow density manually. The research was performed in the application Regression Learner in MATLAB by testing five different premade machine learning models on a dataset. The premade models were, Linear Regression, GPR Matern 5/2, SVM Medium Gaussian, Wide Neural Network and Trilayered Neural Network. Also, the project includes data collection, data cleaning, data modification, data generation, training, testing, and evaluating the models. The results show that air temperature and windspeed overall are the most important parameters and the GPR Matern 5/2 and the Wide Neural Network had the highest performance. Lastly, it was concluded that the machine learning model could be a better alternative to measuring snow density with a real sensor., Målet med detta arbete var att undersöka om en maskininlärningsmodell kunde användas för att förutse snödensitet utifrån sex olika väderparametrar. Dessa var artificiell genererad snödensitet, lufttemperatur, marktemperatur, relativ luftfuktighet, vindhastighet och snödjupsförändring. Frågeställningarna som skulle besvaras var vilka väderparametrar som korrelerar med snödensiteten, vilken eller vilka modeller som presterade bäst samt om maskininlärningsmodellen skulle kunna vara att bättre alternativ till att mäta snödensitet manuellt. Undersökningen utfördes i applikationen Regression Learner i MATLAB genom att testa fem olika förhandsgjorda modeller vilka var Linear Regression, GPR Matern 5/2, SVM Medium Gaussian, Wide neural network och Trilayered neural network. Projektet inkluderar även datainsamling, städning av data, datamodifiering, datagenerering, träning, testning och evaluering av modellerna. Resultaten visar att lufttemperaturen och vindhastigheten över lag är viktigast för modellerna och att GPR Matern 5/2 samt Wide neural network presterade bäst. Slutligen kunde man argumentera för att maskininlärningsmodellen är ett bättre alternativ till att mäta snödensitet manuellt.

В статье представлены результаты исследований характеристик снежного покрова: высоты снега и его плотности, запасов воды в снеге, наблюдавшихся в Беларуси в репрезентативный период 1945–2020 гг. Рассматривается их внутригодовая динамика. Целью данного исследования является оценка пространственно-временной изменчивости характеристик снежного покрова в Беларуси, в том числе анализ внутригодового хода характеристик снежного покрова.

[EN] Spanish latitudes and meteorological conditions cause the snow phenomena to mainly take place in mountainous areas, playing a key role in water resource management, with the Pyrenees as one of the most important and best monitored areas. Based on the most significant dataset of snow density (SDEN) in the Spanish Pyrenees for on-site manual samples and automatic measurements, in this study, single and multiple linear regression models are evaluated that relate SDEN with intra-annual time dependence and other drivers such as the seasonal accumulated precipitation, 7-day average temperatures, snow depth (SD) and elevation. The seasonal accumulated precipitation presented a more dominant influence than daily precipitation, usually being the second most dominant SDEN driver, followed by temperature. Average temperatures showed the best fitting to SDEN. The results showed similar densification rates ranging widely from 0.7 x 10(3) kg/L/day to 2 x 10(3) kg/L/day without showing a spatial pattern. The densification rate for the set of manual samples was set to 1.2 kg/L/day, very similar to the set of automatic measurements (1.3 kg/L/day). The results increase knowledge on SDEN in the Pyrenees. The SDEN regression models that are given in this work may allow us, in the future, to estimate SDEN, and consequently Snow Water Equivalent (SWE), using an economical and extensive SD and meteorological network, although the high spatial variability that has been found must be regarded. Estimating a relationship between SDEN and several climate drivers enables us to take into account the impact of climate variability on SDEN.

type:Glaciology, In the northwest Greenland ice sheet, in-situ measurements of near-surface snow physical conditions, which can be used to estimate surface mass balance, are crucially lacking. Herein, we performed a glaciological field traverse expedition with a traditional Greenlandic dog-sled to obtain in-situ near-surface snow density and stratigraphy data in 2018 spring. We succeeded in obtaining these data from snow pits at four locations. Moreover, we observed the spatial variability of snow density at the SIGMA-A site. These data are in the easy-to-read excel format., source:https://doi.org/10.17592/001.2020091101

Thermal inertia has been successfully used in remote sensing applications that span from geology, geomorphology to hydrology. In this paper, we propose the use of thermal inertia for describing snow dynamics. Two different formulations of thermal inertia were tested using experimental and simulated data related to snowpack dynamics. Experimental data were acquired between 2012 and 2017 from an automatic weather station located in the western Italian Alps at 2,160 m. Simulations were obtained using the one‐dimensional multilayer Crocus model. Results provided evidences that snowmelt phases can be recognized, and average snowpack density can be estimated reasonably well from thermal inertia observations (R2 = 0.71; RMSE = 65 kg/m3). The empirical model was also validated with manual snow density measurements (R2 = 0.80; RMSE = 54 kg/m3). This study is the first attempt at the exploitation of thermal inertia for snow monitoring, combining optical and thermal remote sensing data. Plain Language Summary Alpine snow represents a fundamental reservoir of fresh water at midlatitude. Remote sensing offers the opportunity to estimate snow properties in different spectral domains. In particular, the knowledge of the spatial and temporal variability of snow density could allow modeling of the snow water equivalent, which knowledge is crucial for managing water resources in the face of current climate change. In this study we show for the first time that snow thermal inertia can contribute to monitoring of snowmelt processes and snow density, opening new perspectives for remote sensing of the cryosphere.

Increased temperatures, changed snow conditions, increasing demand for skiing and other winter sports lead to increased demand and production of artificial snow. Besides the positive aspects of artificial snow, it is important to understand potentially negative effects of artificial snow on the environment and vegetation. This study investigated the differences between artificial snow and natural snow in two adjacent ski slopes. This was done by measuring and comparing the snow depth, duration of the snow cover, snow density, and ground vegetation between the slopes. Snow depth and density showed differences between artificial snow and natural snow. The artificial snow cover stayed about 11 days longer than the natural snow. A few variations in the vegetation were also found; earlier flowering and more species and colors in the natural snow slope compared to in the slope using artificial snow. Therefore, it is clear that there is a difference between artificial snow and natural snow, and that this results in effects on the environment, but it is difficult to assess to what extent and how large the negative impact is. For more general conclusions, I think it is important to study the vegetation under artificial snow during a longer period and in several areas.

L’équivalent en eau de le neige ayant des répercussions impactant autant les sphères environnementale, économique et sociale, son estimation suscite toujours l’intérêt de la communauté scientifique. Cette étude a pour but de présenter une nouvelle méthode d’estimation de l’équivalent en eau de la neige afin de pallier aux désavantages des méthodes actuelles. L’équivalent en eau y est estimé par la résolution d’un problème inverse de transfert de chaleur dans la neige où le profil de densité est déterminé à partir de mesures de température. Les méthodes actuelles d’inversion appliquées au couvert de neige ne permettant pas l’obtention d’un profil de densité finement discrétisé, une nouvelle méthode de résolution du problème inverse de transfert de chaleur dans la neige est proposée. Les flux de chaleur dans la neige sont successivement estimés tout en comparant les profils de température calculés et observés au sein du couvert jusqu’à ce qu’un bon accord soit atteint entre ces derniers dans le but d’estimer les profils de conductivité thermique et de densité. Cette approche rend possible l’obtention d’un profil de densité finement discrétisé, permettant une estimation précise de l’équivalent en eau de la neige. Un cas synthétique a été construit afin de démontrer l’efficacité et la robustesse de la méthode. Il est démontré que les profils de conductivité thermique et de densité de la neige peuvent être récupérés à partir de mesures de température par l’approche proposée. La précision des paramètres estimés dépend par contre fortement de la précision des mesures de température. De plus, la connaissance du profil de température et des caractéristiques physiques de la neige précédemment estimées permet la détermination du flux de chaleur total aux frontières du couvert de neige, fournissant ainsi de l’information particulièrement utile pour l’étude des échanges énergétiques entre le sol, la neige et l’atmosphère. Impacting environmental, economic and social s

This paper presents one-dimensional numerical model for snowmelt/accumulation simulations, based on the equation of heat energy. It is assumed that the snow column is homogeneous at the current time step; however, its characteristics such as snow density and thermal conductivity are treated as functions of time. The equation of heat energy for snow column is solved using the implicit finite difference method. The incoming energy at the snow surface includes the following parts: conduction, convection, radiation and the raindrop energy. Along with the snow melting process, the model includes a model for snow accumulation. The Euler method for the numerical integration of the balance equation is utilized in the proposed model. The model applicability is demonstrated at the meteorological station Zlatibor, located in the western region of Serbia at 1,028 meters above sea level (m.a.s.l.) Simulation results of snowmelt/accumulation suggest that the proposed model achieved better agreement with observed data in comparison with the temperature index method. The proposed method may be utilized as part of a deterministic hydrological model in order to improve short and long term predictions of possible flood events.

This paper presents one-dimensional numerical model for snowmelt/accumulation simulations, based on the equation of heat energy. It is assumed that the snow column is homogeneous at the current time step; however, its characteristics such as snow density and thermal conductivity are treated as functions of time. The equation of heat energy for snow column is solved using the implicit finite difference method. The incoming energy at the snow surface includes the following parts: conduction, convection, radiation and the raindrop energy. Along with the snow melting process, the model includes a model for snow accumulation. The Euler method for the numerical integration of the balance equation is utilized in the proposed model. The model applicability is demonstrated at the meteorological station Zlatibor, located in the western region of Serbia at 1,028 meters above sea level (m.a.s.l.) Simulation results of snowmelt/accumulation suggest that the proposed model achieved better agreement with observed data in comparison with the temperature index method. The proposed method may be utilized as part of a deterministic hydrological model in order to improve short and long term predictions of possible flood events.

A new parameterization of snow water equivalent (SWE) based on snow depth (HS)has been developed from observations made in northern Sweden. When applyingprevious SWE parameterization from the Alps on observations from northern Sweden,the SWE values are systematically 20% lower. The new SWE parameterization isderived from a snow layer density regression model using snow layer hardness andsnow particle size. The model was evaluated with a detailed field reference dataset,and then applied to the long term Abisko Scientific Research Station stratigraphicsnowpack dataset. The model enables a regional adjustment of snow layer densityvalues for northern Swedish conditions. The snow layer density model provides anaccurate estimation of snow bulk density used to derive the SWE parameterizationbased solely on HS. Snow depth observations are regularly made on a daily basis; byapplying our new parameterization, daily values of SWE can be obtained for northernScandinavian conditions, which e.g. can be used for hydropower production planningand risk assessments.

A new parameterization of snow water equivalent (SWE) based on snow depth (HS) has been developed from observations made in northern Sweden. When applying previous SWE parameterization from the Alps on observations from northern Sweden, the SWE values are systematically 20% lower. The new SWE parameterization is derived from a snow layer density regression model using snow layer hardness and snow particle size. The model was evaluated with a detailed field reference dataset, and then applied to the long-term Abisko Scientific Research Station stratigraphic snowpack dataset The model enables a regional adjustment of snow layer density values for northern Swedish conditions. The snow layer density model provides an accurate estimation of snow bulk density used to derive the SWE parameterization based solely on HS. Snow depth observations are made on a daily basis; by applying our new parameterization, daily values of SWE can be obtained for northern Scandinavian conditions, which can be used, for example, for hydropower production planning and risk assessments.

This report demonstrates the snow water equivalent (SWE) modeling capabilities present within the Gridded Surface Sub-surface Hydrologic Analysis (GSSHA) model (Downer and Ogden 2004, 2006) by comparing simulated and observed snow accumulation data. GSSHA is used to simulate runoff, streamflow, and sediment and constituent transport in military and civil works projects. An energy balance method was originally used for calculating snow accumulation and melt, but now GSSHA has an optional temperature-index method and hybrid energy method. The full snowpack modeling capabilities of the Snowmelt Numerical-Analytical Package (SNAP) model (Albert and Krajeski 1998) have been included to simulate snowpack depth and density regardless of which snow accumulation model was used. With proficient SWE modeling capabilities, the GSSHA model can be utilized to solve complex watershed-related issues in regions where snow accumulation and melt are often the most critical source of water.

An accurate assessment of snow depth and snow density is essential to determine that amount of water stored in the snowpack, i.e., snow water equivalent (SWE). The measurement of snow density is much more difficult and time consuming than snow depth. The variability in snow density is evaluated for a 5.4-km stretch of the Rio Esera headwaters in the Spanish Pyrenees Mountains. The traditional snow tube method is compared to the more labour-intensive but accurate snow pit method. The former method measures snow depth and extracts a snow core that is weighed. The latter method uses a wedge cutter to extract a 1-L snow sample to estimate density at 10-cm intervals through the snowpack. The variability in snowpack density is not systematic and can only be explained at lower elevation when the snowpack is known to be melting, as identify by an isothermal snowpack at zero degrees Celsius. This occurred during a mid-January survey. A late-April survey showed that these lower elevation sites were still more dense., Determinar con exactitud el espesor y densidad del manto de nieve resulta necesario para conocer su contenido de agua. La medición de la densidad resulta mucho más difícil y requiere más tiempo que la estimación de su espesor. En este trabajo se evalúa la variabilidad espacial de la densidad de la nieve en un tramo de 5.4 kilómetros en la cabecera del río Ésera (Pirineos Españoles). El sistema tradicional con un muestreador de nieve (snow tube sampler) es comparado con un sistema más laborioso, pero preciso, basado en la medición estratificada de la densidad a lo largo de toda la profundidad del manto de nieve. El primer método se basa en la extracción de una muestra de todo el manto de nieve con un tubo, y derivar su densidad mediante el conocimiento de el espesor y peso de la muestra. El segundo consiste en extraer muestras de un 1-L de volumen de nieve cada diez centímetros, desde la superficie nevada hasta el suelo. A mediados de enero, la variabilidad del manto de nieve no resulta sistemática espacialmente, y sólo puede ser explicada en las zonas de menor altura, dónde el manto de nieve esta próximo a condiciones de fusión, dada su proximidad a condiciones isotermas (próximo a los 0ºC). A finales de abril, los puntos de medición situados a menor altura también mostraron la mayor densidad

An accurate assessment of snow depth and snow density is essential to determine that amount of water stored in the snowpack, i.e., snow water equivalent (SWE). The measurement of snow density is much more difficult and time consuming than snow depth. The variability in snow density is evaluated for a 5.4-km stretch of the Rio Esera headwaters in the Spanish Pyrenees Mountains. The traditional snow tube method is compared to the more labour-intensive but accurate snow pit method. The former method measures snow depth and extracts a snow core that is weighed. The latter method uses a wedge cutter to extract a 1-L snow sample to estimate density at 10-cm intervals through the snowpack. The variability in snowpack density is not systematic and can only be explained at lower elevation when the snowpack is known to be melting, as identify by an isothermal snowpack at zero degrees Celsius. This occurred during a mid-January survey. A late-April survey showed that these lower elevation sites were still more dense., Determinar con exactitud el espesor y densidad del manto de nieve resulta necesario para conocer su contenido de agua. La medición de la densidad resulta mucho más difícil y requiere más tiempo que la estimación de su espesor. En este trabajo se evalúa la variabilidad espacial de la densidad de la nieve en un tramo de 5.4 kilómetros en la cabecera del río Ésera (Pirineos Españoles). El sistema tradicional con un muestreador de nieve (snow tube sampler) es comparado con un sistema más laborioso, pero preciso, basado en la medición estratificada de la densidad a lo largo de toda la profundidad del manto de nieve. El primer método se basa en la extracción de una muestra de todo el manto de nieve con un tubo, y derivar su densidad mediante el conocimiento de el espesor y peso de la muestra. El segundo consiste en extraer muestras de un 1-L de volumen de nieve cada diez centímetros, desde la superficie nevada hasta el suelo. A mediados de enero, la variabilidad del manto de nieve no resulta sistemática espacialmente, y sólo puede ser explicada en las zonas de menor altura, dónde el manto de nieve esta próximo a condiciones de fusión, dada su proximidad a condiciones isotermas (próximo a los 0ºC). A finales de abril, los puntos de medición situados a menor altura también mostraron la mayor densidad

[ES] Determinar con exactitud el espesor y densidad del manto de nieve resulta necesario para conocer su contenido de agua. La medición de la densidad resulta mucho más difícil y requiere más tiempo que la estimación de su espesor. En este trabajo se evalúa la variabilidad espacial de la densidad de la nieve en un tramo de 5.4 kilómetros en la cabecera del río Ésera (Pirineos Españoles). El sistema tradicional con un muestreador de nieve (snow tube sampler) es comparado con un sistema más laborioso, pero preciso, basado en la medición estratificada de la densidad a lo largo de toda la profundidad del manto de nieve. El primer método se basa en la extracción de una muestra de todo el manto de nieve con un tubo, y derivar su densidad mediante el conocimiento de el espesor y peso de la muestra. El segundo consiste en extraer muestras de un I-L de volumen de nieve cada diez centímetros, desde la superficie nevada hasta el suelo. A mediados de enero, la variabilidad del manto de nieve no resulta sistemática espacialmente, y sólo puede ser explicada en las zonas de menor altura, donde el manto de nieve está próximo a condiciones de fusión, dada su proximidad a condiciones isotermas (próximo a los O°C). A finales de abril, los puntos de medición situados a menor altura también mostraron la mayor densidad., [IN] An accurate assessment of snow depth and snow density is essential to determine that amount of water sto red in the snowpack, i. e., snow water equivalent (SWE). The measurement of snow density is much more difficult and time consuming than snow depth. The variability in snow density is evaluated for a 5.4-km stretch of the Rio Esera headwaters in the Spanish Pyrenees Mountains. The traditional snow tube method is compared to the more labourintensive but accurate snow pit method. The former method measures snow depth and extracts a snow core that is weighed. The latter method uses a wedge cutter to extract a l-L snow sample to estimate density at lO-cm intervals through the snowpack. The variability in snowpack density is not systematic and can only be explained at lower elevation when the snowpack is known to be melting, as identifY by an isothermal snowpack at zero degrees Celsius. This occurred during a mid-January survey. A late-April survey showed that these lower elevatíon sites were still more dense.

During April 2007, a coordinated series of snow measurements were made across the Northwest Territories and Nunavut, Canada, during a 4200-km snowmobile traverse from Fairbanks, Alaska, to Baker Lake, Nunavut. While detailed, local snow measurements have been made as part of ongoing studies at tundra field sites in this region (Daring Lake and Trail Valley Creek in the Northwest Territories), systematic measurements at the regional scale have not been previously collected across this region. Consistent with observations of tundra snow in Alaska and northern Manitoba, the snow cover consisted of depth hoar and wind slab with small and ephemeral fractions of new, recent, and icy snow. The snow was shallow (less than 40 cm deep), usually with less than six layers. Where deposited on lake and river ice, the snow was shallower, denser, and more metamorphosed than where deposited on tundra. The snow characteristics were highly variable at a local scale, but no longitudinal gradients in snow distribution, magnitude, or structure were detected. Lakes and lake ice confounded passive microwave remote sensing of the snow cover in this area because the lake signal overwhelmed the snow signal. Consequently, challenges remain in developing methods to monitor this snow cover by satellite. Appendixes present near-infrared images, snow depths, soot measurements, mercury measurements, ion measurements, and isotope measurements., Prepared in cooperation with the Climate Research Division, Environment Canada, Toronto, Ontario; the Cooperative Institute for Research and Engineering in the Atmosphere, Colorado State University, Fort Collins, CO; the University of Alaska Fairbanks, Fairbanks, AK; and Huntington Consulting, Anchorage, AK. The original document contains color images.

Vertical soundings of the snowpack are essential diagnostic tools for snow hydrologists and avalanche forecasters. Hydrologists require quantitative profiles of snow density from which they can infer the total amount of snow coverage in a region. Because variations in altitude and terrain can result in widely different coverage, they must dig a relatively large number of pits or extract and weigh many core samples to estimate the snow water equivalent from the corresponding average density. Avalanche forecasters are concerned with recent precipitation and relatively rapid metamorphosis of buried snow layers. While they too require a large number of pits to assess the avalanche potential of a basin, they often perform a more qualitative inspection aimed at identifying the presence and depth of weak layers in a relatively immature snowpack. To address these needs, we have developed a penetration field-portable capacitance probe capable of recording profiles of complex dielectric permittivity and temperature through the snowpack. Density profiles were acquired without the need for multiple excavations. The probe consists of a lance with a wedged capacitance tip allowing penetration to variable depths. The tip is connected to a portable signal conditioner for data processing and storage., Prepared in cooperation with Cornell Univ., Sibley School of Mechanical and Aerospace Engineering, Ithaca, NY.

We describe a penetration field-portable capacitance probe capable of recording profiles of dielectric permittivity through the snow pack. The probe was developed under the auspices of the Army Research Office as a Phase I SBIR project. It consists of a wedged capacitance tip, which is mounted at the end of a pole allowing its penetration through depths of at least 2 m. The capacitance instrument is integrated in the tip. By appropriate placement of its ground, guard and sensor conductive surfaces, the probe sheds horizontal electric field lines permitting it to resolve horizontal snow layers of 2.5 mm., Prepared in cooperation with Cornell University, Upson Hall, Ithaca, NY 14853.

Snow cover data in Arctic regions like Svalbard are scarce while interest in distribution, properties and development of Arctic snow is evident (in context of climate change, too). Contributing to that issue this work is based on comprehensive observations of physical snow parameters recorded in snow pits and using special instruments, respectively. The data were collected in spring 2011 and at several locations in a height transect along Kongsvegen glacier. Analysis mainly concerns description of gross profile characteristics, the identification and physical understanding of major structures and their spatial and temporal characteristics. Three of them are considered in detail in which context snow hardness data are particularly important. Analysis was supported by data from automatic weather stations operated at the sites of snow investigations. These data also served to drive a numerical snow model. Model output was compared to observations and effectively served development of a process-based interpretation and reconstruction of the history of the investigated key layers. Different depth scaling method were developed to make the snow profiles comparable because snow depth strongly depends on altitude. The layer-wise depth scaling method yielded significant improvements in correlation between neighbouring snow hardness profiles along the glacier and at local scale. Three layers characterized by clear maxima in snow hardness, high density and mostly rounded grain types could thus be traced along the major part of the glacier. Attributions were comparatively uncertain if not impossible in the lower section of the glacier due to the higher temperatures and associated melt influences. Snow profiles were significantly correlated across decameter distances. The evolution history of the selected, layers was investigated using relevant model output data. The latter were overall successfully validated by comparison with measured key parameters like snowheight, surface temperature or end-winter vertical profiles. Some deficits in simulation skill were reflected in e.g. biases towards too low snow temperature and failure to reproduce fine structures in the vertical density profiles. This points to a need to improve related aspects of the model setup and parameterizations. There are indications that some problems may lead back to inappropriate parameterization of the density of fresh snow. Concerning the investigated examplary layers, however, the simulations largely reproduce the observed characteristics. Uncerainties are generally larger at lower sites again, and a general tendency towards too rapidly evolving snow metamorphosis may be noted. Backtracing individual layers proved feasible and allows estimating the approximate date of deposition and associated meteorological conditions. Follow up work may concern even more detailed analysis of the observed profiles, including parameters and sites which have not yet been considered in this work. Advancing methods for more sophisticated depth attribution of profile data is desirable as well as developing enhanced understanding of processes governing the development of observed physical properties (grain types in particular). Related simulations may mainly be improved concerning their setup and parameterizations (of fresh snow density for example). On the other hand, existing output already hints on interesting new research topics related to e.g. the development of a firn aquifer in the upper regions of the glacier and its dependencies on near- surface snow developments, by Maik Brakemeier, Masterarbeit University of Innsbruck 2017

Snow cover data in Arctic regions like Svalbard are scarce while interest in distribution, properties and development of Arctic snow is evident (in context of climate change, too). Contributing to that issue this work is based on comprehensive observations of physical snow parameters recorded in snow pits and using special instruments, respectively. The data were collected in spring 2011 and at several locations in a height transect along Kongsvegen glacier. Analysis mainly concerns description of gross profile characteristics, the identification and physical understanding of major structures and their spatial and temporal characteristics. Three of them are considered in detail in which context snow hardness data are particularly important. Analysis was supported by data from automatic weather stations operated at the sites of snow investigations. These data also served to drive a numerical snow model. Model output was compared to observations and effectively served development of a process-based interpretation and reconstruction of the history of the investigated key layers. Different depth scaling method were developed to make the snow profiles comparable because snow depth strongly depends on altitude. The layer-wise depth scaling method yielded significant improvements in correlation between neighbouring snow hardness profiles along the glacier and at local scale. Three layers characterized by clear maxima in snow hardness, high density and mostly rounded grain types could thus be traced along the major part of the glacier. Attributions were comparatively uncertain if not impossible in the lower section of the glacier due to the higher temperatures and associated melt influences. Snow profiles were significantly correlated across decameter distances. The evolution history of the selected, layers was investigated using relevant model output data. The latter were overall successfully validated by comparison with measured key parameters like snowheight, surface temperature or end-winter vertical profiles. Some deficits in simulation skill were reflected in e.g. biases towards too low snow temperature and failure to reproduce fine structures in the vertical density profiles. This points to a need to improve related aspects of the model setup and parameterizations. There are indications that some problems may lead back to inappropriate parameterization of the density of fresh snow. Concerning the investigated examplary layers, however, the simulations largely reproduce the observed characteristics. Uncerainties are generally larger at lower sites again, and a general tendency towards too rapidly evolving snow metamorphosis may be noted. Backtracing individual layers proved feasible and allows estimating the approximate date of deposition and associated meteorological conditions. Follow up work may concern even more detailed analysis of the observed profiles, including parameters and sites which have not yet been considered in this work. Advancing methods for more sophisticated depth attribution of profile data is desirable as well as developing enhanced understanding of processes governing the development of observed physical properties (grain types in particular). Related simulations may mainly be improved concerning their setup and parameterizations (of fresh snow density for example). On the other hand, existing output already hints on interesting new research topics related to e.g. the development of a firn aquifer in the upper regions of the glacier and its dependencies on near- surface snow developments, by Maik Brakemeier, Masterarbeit University of Innsbruck 2017