Improved knowledge of the cryosphere state is paramount for continued model development and for accurate estimates of fresh water supply. This work focuses on evaluation and potential improvements of current snow datasets over the United States. Snow in mountainous terrain is most difficult to quantify due to the slope, aspect, and remote nature of the environment. Due to the difficulty of measuring snow quantities in the mountains, the initial study creates a new method to upscale point measurements to area averages for comparison to initial snow quantities in numerical weather prediction models. The new method is robust and cross validation of the method results in a relatively low mean absolute error of 18% for snow depth (SD). Operational models at the National Centers for Environmental Prediction which use Air Force Weather Agency (AFWA) snow depth data for initialization were found to underestimate snow depth by 77% on average. Larger error is observed in areas that are more mountainous. Additionally, SD data from the Canadian Meteorological Center, which is used for some model evaluations, performed similarly to models initialized with AFWA data. The use of constant snow density for snow water equivalent (SWE) initialization for models which utilize AFWA data exacerbates poor SD performance with dismal SWE estimates. A remedy for the constant snow density utilized in NCEP snow initializations is presented in the next study which creates a new snow density parameterization (SNODEN). SNODEN is evaluated against observations and performance is compared with offline land surface models from the National Land Data Assimilation System (NLDAS) as well as the Snow Data Assimilation System (SNODAS). SNODEN has less error overall and reproduces the temporal evolution of snow density better than all evaluated products. SNODEN is also able to estimate snow density for up to 10 snow layers which may be useful for land surface models as well as conversion of remotely-sensed SD to SWE. Due to the poor performance of previously evaluated snow products, the last study evaluates openly-available remotely-sensed snow datasets to better understand the strengths and weaknesses of current global SWE datasets. A new SWE dataset developed at the University of Arizona is used for evaluation. While the UA SWE data has already been stringently evaluated, confidence is further increased by favorable comparison of UA snow cover, created from UA SWE, with multiple snow cover extent products. Poor performance of remotely-sensed SWE is still evident even in products which combine ground observations with remotely-sensed data. Grid boxes that are predominantly tree covered have a mean absolute difference up to 87% of mean SWE and SWE less than 5 cm is routinely overestimated by 100% or more. Additionally, snow covered area derived from global SWE datasets have mean absolute errors of 20%-154% of mean snow covered area.

Yan, Su. / "December 2010"--Abstract. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 154-165). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

The friction of tyres on roads has been of practical importance for many years with nearly 80% of terrestrial traffic making use of rubber tyres. Tyres provide the grip required for vehicle acceleration, braking and cornering. In order for a tyre to grip on a snow covered surface friction mechanisms such as “ploughing”, (where sharp tread block edges dig into and break bonds between the snow grains) and fluid film lubrication must be considered. These are not present when a tyre interacts with tarmac. In addition metamorphism of the snow over time can result in variations of the structure and mechanical properties, this can occur rapidly especially when dealing with temperatures close to snows melting point. When full car-scale outdoor testing is carried out the snow conditions cannot be controlled and vary daily. This means the snow properties must be measured every day so that any observed variations in friction can be attributed to the tyres rather than the snow. At present the simple measurements being carried out on the snow tracks have not proved sufficient to pick up on the variations in the snow. This leads to inconsistent results: one tyre behaves differently on two different days, even though the snow was measured to be the same. This has resulted in the need for further study of the way snow variations influence the grip of winter tyres. The primary aim of this study is to identify which snow properties contribute to the friction of tyres on snow and be able to estimate the friction from measurements of snow properties. This work is the first comprehensive study to combine: multiple snow properties, microstructure characterisation, measurement of friction behaviour and different snow (both artificial and natural). In order to study the way snow affects the grip of winter tyres, methods of manufacturing artificial snow with consistent mechanical properties and microstructure are used. A method of blending ice chips (a solid state fracturing process) and compressing the resulting snow to form a test track was developed during a previous PhD carried out in our group. An alternate snow microstructure was created by using an established process of creating snow by vapour deposition. The process was simplified and downscaled, the resulting snow consisted of large dendritic grains, very different to the blended ice chips. Both snows were pressed in identical manners to create snow testing tracks. In addition, natural snow collected from the field was tested to compare with the artificial snow. In order to investigate how the variations in the snow affected the friction of tyres extensive testing was carried out in a cold room using a linear tribometer, using procedures established in previous studies. Two analytical rubber samples were used to investigate the friction, a rounded edge sample and a siped sample. Testing was carried out at -10°C at speeds of 0.01m/s, 0.1m/s and 1m/s. A significant part of this PhD involved the development of new methods and equipment which have not been used to study snow in this way before. In order to characterise mechanical properties, shear testing, compression testing and cohesion testing were carried out. To investigate snow microstructure, surface profilometry, microscopy and X-ray microtomography were used. Correlating the changes observed in snow characteristics with the changes recorded in the coefficient of friction has allowed the development of an empirical equation. This can be used to predict the coefficient of friction of a given snow based on three relatively simple snow measurements: a compression test to calculate the effective modulus, a roughness measurement to calculate the peak count density and a snow penetration test. For the first time this study allows us to use the empirical equation to estimate the relative contributions of the ploughing and surface friction mechanisms to the total friction. This allows the comparison of full car-scale test data as it is now possible to account for variations in the snow test tracks.

Accurate areal measurements of snow-cover extent are important for hydrological and climate modeling. The traditional method of mapping snow cover is binary where a pixel is approximated to either snow-covered or snow-free. Fractional snow cover (FSC) mapping achieves a more precise estimate of areal snow-cover extent by determining the fraction of a pixel that is snow-covered. The two most common FSC methods using Moderate Resolution Imaging Spectroradiometer (MODIS) images are linear spectral unmixing and the empirical Normalized Difference Snow Index (NDSI) method. Machine learning is an alternative to these approaches for estimating FSC, as Artificial Neural Networks (ANNs) have been used for estimating the subpixel abundances of other surfaces. The advantages of ANNs over the other approaches are that they can easily incorporate auxiliary information such as land-cover type and are capable of learning nonlinear relationships between surface reflectance and snow fraction. ANNs are especially applicable to mapping snow-cover extent in forested areas where spatial mixing of surface components is nonlinear. This study developed an ANN approach to snow-fraction mapping. A feed-forward ANN was trained with backpropagation to estimate FSC from MODIS surface reflectance, NDSI, Normalized Difference Vegetation Index (NDVI) and land cover as inputs. The ANN was trained and validated with high spatial-resolution FSC derived from Landsat Enhanced Thematic Mapper Plus (ETM+) binary snow-cover maps. ANN achieved best result in terms of extent of snow-covered area over evergreen forests, where the extent of snow cover was slightly overestimated. Scatter plot graphs of the ANN and reference FSC showed that the neural network tended to underestimate snow fraction in high FSC and overestimate it in low FSC. The developed ANN compared favorably to the standard MODIS FSC product with the two methods estimating the same amount of total snow-covered area in the test scenes.

This study quantifies spatial and temporal patterns in snow water equivalent (SWE), chemistry, and water isotopes associated with snowpack shading due to aspect and vegetation in the Valles Caldera National Preserve, New Mexico. Depth, density, stratigraphy, temperature, and snow chemistry, isotope, and biogeochemical nutrient samples were collected and analyzed from five snowpit locations on approximate monthly intervals between January-April 2007. SWE showed little variability between sites in January (~10mm) but differences expanded to 84mm (30%) by max accumulation in open sites and 153mm (45%) between all sites. Sulfate varied by 22% (10.6-13.5 microeq/L), Cl- by 35% (17.4-26.9 microeq/L), and d18O by 17% (-16.3 to -13.5), with SWE exhibiting inverse correlations with d18O (r2=0.96), SO42- (r2=0.75), and Cl- (r2=0.60) at max accumulation. Regression relationships suggest variability in SWE and solutes/water isotopes are primarily driven by sublimation. Mass balance techniques estimate sublimation ranges from 1-16% between topographically- and non-shaded open sites.

Note: / The purpose of this study was to develop and evaluate a numerical model for simulating the influence of seasonal snow cover on the ground thermal regime.The physical processes occurring within the snow pack and underlying ground, as well as at the surface, are reviewed from the viewpoint of their eventual inclusion in a practical numerical model. A finite difference heat conduction model suitable for long-term studies of the snow-ground thermal interaction is presented. This model incorporates a numerical prediction of density and temperature profiles within an evolving snow cover. Phase change within the ground is treated by a new numerical method which has important computational advantages over older methods.[...] / Cette etude avait pour but Ie developpement et l'evaluation d'un modele numerique pour la simulation de l'influence du manteau neigeux saisonier sur Ie regime thermique du sol.Les processus physiques rencontres dans Ie manteau neigeux et Ie sol sous-jacent ainsi quIa l'interface air-surface sont discutes et passes en revue dans l'optique restreinte de leur eventuelle inclusion dans un modele numerique. On presente ensuite un modele a conduction en differences finies qui convient pour les etudes de l'interaction thermique neige-sol a long terme. Le modele inclut un calcul numerique de l'evolution temporelle des profiles de densite et de temperature du manteau neigeux. Les changements de phase dans Ie sol sont traites par une nouvelle technique numerique avantageuse pour Ie calcul.[...]

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Univ., Diss.--Bremen, 2006. / Richtiger Name des Verf.: Nicolaus, Marcel.

Thesis (Ph. D.)--University of Washington, 2000. / Vita. Includes bibliographical references (leaves 154-161).