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Scientific Reality in C. P. SnowDamico, Dorothy Trageser 04 1900 (has links)
Twentieth-century science proves that heredity and environment function similarly in all named living species except one--Homo sapiens. Man alone, through his intellect, forms language and culture, thereby affecting his environment so that he participates in the process of his own creation. This participation so links humans that each man extends outside himself creating of the human race a single, whole fabric. C. P. Snow, aware of this communal reality, notes the present lack of communication between scientists and humanists. He contends that this lack, described as the two-cultures split, endangers both the practical survival of Western civilization and mankind's understanding of its own humanity. This study analyzes modern scientific reality and shows that Snow's articles, lectures, and novels articulate that reality and confirm the merit of Snow's observations.
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Analyse des effets de la végétation sur le couvert de neige dans la zone de transition arctique-subarctique par mesures in-situ et télédétection optique (Nunavik)Busseau, Bruno-Charles January 2017 (has links)
Plusieurs études récentes démontrent que la prolifération de la végétation dans le Nord a augmenté sous un climat en réchauffement lors des quatres dernières décennies, surtout dans la zone de transition entre toundra et taïga. L’accroissement des arbustes a un effet sur les propriétés de la neige et du bilan d’énergie de surface. L’objectif de cette recherche est d’améliorer la caractérisation de l’impact des arbustes sur l’évolution de la neige (accumulation et fonte) en utilisant des données terrains et la télédétection. La recherche a été réalisée sur le site d’Umiujaq, au Nunavik, représentatif de la zone de transition entre l’Arctique de basse latitude et les zones subarctiques. La profondeur de neige, mesurée le long de nombreux transects qui couvrent plusieurs types de végétation (toundra arbustive, toundra de lichen, forêt ouverte et forêt fermée d’épinettes) démontre l’effet d’emprisonnement de la neige dans la zone de transition entre une zone de toundra arbustive vers une zone de forêt d’épinettes. Cet effet est lié à la hauteur de la végétation et à la perte de densité (la profondeur de neige augmente par des facteurs de 2,5 à 3). De plus, des mesures de profondeur de neige en continue ont été prises par deux stations météorologiques situées l'une en zone de toundra arbustive et l'autre en zone de forêt. Les résultats montrent que la neige réagit de façons très différentes selon la couverture du sol, mais reste très dépendante des sites considérés. Des analyses spatiales à très haute résolution (Pléiades) et à moyenne résolution (Landsat et MODIS) suggèrent un délai dans la fonte entre les zones de forêts et les zones de toundra de lichen et arbustives. Une technique de mesure de profondeur de neige par télédétection à haute résolution est aussi discutée. / Abstract : 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 to 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. A technic to measure snow depth using high resolution is also discussed.
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The influence of the North Atlantic oscillation on seasonal snowfall totals in the northeastern United States, 1961-2010Widen, Holly M. 21 July 2012 (has links)
The North Atlantic Oscillation (NAO) is one of the main components of atmospheric circulation variability within the middle and high latitudes of the Northern Hemisphere and largely affects winter weather in northeastern United States. This study examined the most recent decadal trend of the NAO as well as its influence on snowfall totals and storm track variability in the northeast U.S. over the previous 50-year period. Previous research has indicated greater snowfall totals in the Northeast during NAO negative phases due to repeated polar outbreaks. Nonetheless, past research has also shown connections between the NAO positive phase and active winter seasons in this region. This study provides insight on how both positive and negative NAO phases can produce significant snowfall in the Northeast. Statistical and graphical analysis were completed to assess the relationship between the NAO and seasonal snowfall (NDJFM) from 1961-2010 for stations within the Northeast (Virginia to Maine). In addition, two case studies of recent winter events with differing NAO phases were evaluated to provide insight on how both NAO phases can produce significant snowfall in the Northeast.
The statistical analysis revealed inverse relationships between the NAO negative phase and seasonal snowfall. The composite analysis indicated an average positive NAO pattern from 1961-2010, yet the NAO negative years produced higher frequency of snowfall in the Northeast. The case studies highlighted variations in storm track and snowfall distribution of the two winter events in differing phases. This study shows that snowfall can occur in particular regions of the Northeast regardless of the NAO phase which has important implications for forecasters. This research also provides the necessary information to complete the most recent decadal trend of the NAO and determine its average pattern. The update of this record will assist climatologists and weather forecasters in predicting future northeast U.S. winter storms. / Department of Geography
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The impact of the radiation balance on snowmelt in a sparse deciduous birch forestTurton, Rachael Heather January 2017 (has links)
The representation of high-latitude surface processes and quantifying surface-climate feedbacks are some of the most serious shortcomings of present day Arctic land surface modelling. The energy balance of seasonally snow-covered sparse deciduous forests at high latitudes is poorly understood and inaccurately represented within hydrological and climate models. Snow cover plays an important role in wintertime fluxes of energy, water and carbon, controlling the length of the active growing season and hence the overall carbon balance of Arctic ecosystems. Snow cover is non-uniform and spatially variable, as wind redistributes snow from areas of exposed open tundra to sheltered areas within the forest, where a deeper snowpack develops. Low solar zenith angles, coupled with sparse deciduous leafless trees, cast shadows across the snow surface. The spatial distribution of canopy gaps determines the timing of direct radiation which penetrates down through the canopy to the snow surface. The forest canopy also excludes incoming longwave radiation and yet also emits longwave radiation to the snow surface. Consequently the forest canopy plays a key role in the radiation balance of sparse forests. To improve our knowledge of these complex processes, meteorological and field observations were taken in an area of highly heterogeneous birch Betula pubescens ssp. czerepanovii forest in Abisko, Sweden during the spring of 2008 and 2009. Detailed measurements of short and longwave radiation above and below the canopy, hemispherical photographs, tree temperatures and snow surveys were conducted to quantify the radiation balance of the sparse deciduous forest. An array of below canopy pyranometers found the mean canopy transmissivity to be 74 % in 2008 and 76 % in 2009. Hemispherical photographs taken at the pyranometer locations analysed with Gap Light Analyzer (GLA) showed reasonable agreement with a mean canopy transmissivity of 75 % in 2008 and 74 % in 2009. The canopy transmissivity was found to be independent of the diffuse fraction of radiation as the canopy is very sparse. A series of survey grids and transects were established to scale up from the below canopy pyranometers to the landscape scale. Hemispherical photographs analysed with GLA showed the sparse forest canopy had a mean transmissivity of 78 % and a mean LAI of 0.25, whereas the open tundra had a mean transmissivity of 97 % and a mean LAI of < 0.01. Snow surveys showed the sparse forest snow depth to vary between 0.34 and 0.55 m, whereas the snow depth in the open tundra varied between 0.12 and 0.18 m. Observations of canopy temperatures showed a strong influence of incident shortwave radiation warming the tree branches to temperatures up to 15 °C warmer than ambient air temperature on the south facing sides of the trees, and up to 6 °C on the north facing sides of the trees. To reproduce the observed radiation balance, two canopy models (Homogenous and Clumped) were developed. The Homogeneous canopy model assumes a single tree tile with a uniform sparse canopy. The Clumped canopy model assumes a tree and a grass tile, where the tree tile is permanently in shade from the canopy and the grass tile receives all the incoming radiation. These canopy models identified the need for a parameter that accounts for the spatial and temporal variation of the shaded gaps within the sparse forest. JULES (Joint UK Land Environment Simulator) is the community land surface model used in the UK Hadley Centre GCM suite. Modifications of the land-surface interactions were included in JULES to represent the shaded gaps within the sparse deciduous forest. New parameterisations were developed for the time-varying sunlit fractions of the gap (flit), the sky-view fraction (fv), and the longwave radiation emitted from the canopy (LWtree). These model developments were informed by field observations of the forest canopy and evaluated against the below canopy short and longwave radiation observed data sets. The JULES Shaded gap model output showed a strong positive relationship with the observations of below canopy shortwave and longwave radiation. The JULES Shaded gap model improves the ratio of observed to modelled short and longwave radiation on sunny days compared to the JULES model. The JULES Shaded gap model reduces the time to snow melt by 2 to 4 days compared to the JULES model, making the model output more aligned with in-situ observational data. This shortening of the modelled snow-season directly impacts on the simulated carbon and water balance regionally and has wider relevance at the pan-Arctic scale. When JULES Shaded Gap was evaluated on the global scale, it improved the modelled snowmass across large areas of sparse forest in northern Canada, Scandinavia and Northern Russia with respect to GlobSnow. The performance of the land surface-snow-vegetation interactions of JULES was improved by using the Shaded gap to model the radiation balance of sparse forests in climate-sensitive Arctic regions. Furthermore these observational data can be used to develop and evaluate high latitude land-surface processes and biogeochemical feedbacks in other earth system models.
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Investigating Seasonal Snow in Northern Sweden – a Multi-Layer Snow Pack Model and Observations from Abisko Scientific Research Station Provide Clues / Undersökning av säsongssnö i norra Sverige – ledtrådar från en snölagermodell samt observationer vid Abisko naturvetenskapliga stationStaffansdotter, Anna January 2017 (has links)
Meteorological parameters determine the physical properties of snow precipitating from the atmosphere, but snow layers also continue to develop within the snow pack after the precipitation event. New characteristics form depending on temperature fluctuations, interaction with the soil, overburden compression, rain-on-snow events and more. As climate change is evidenced across the globe and particularly in the Arctic, understanding the relationship between snow and climate is important. In this project, a set of observed data of snow layer characteristics, collected every two weeks each winter over a 50+ year period at Abisko Scientific Research Station, northern Sweden, is co-studied with a multi-layer snow pack model which is able to reproduce additional snow properties. Data is presented in long time series as well as in high resolution to capture both trends and details. Comparison between modelled and observed data is made where possible. Physical processes are discussed and potential trends in the data are evaluated. Results show good agreement for snow pack depth between model and observations, while modelled snow density is largely confirmed by comparison with other records of density measured at Abisko. Modelled outputs illustrate snow pack temperature fluctuations, percolation of melt water and densification of snow layers within the profiles; observed data show variations in snow layer hardness, grain compactness, grain size and dryness. Long-term trends indicate an increase in snow layer hardness and a decrease in snow grain size since the beginning of the record. / Förhållanden i atmosfären bestämmer vilken sorts snö som fälls ut som nederbörd, men de snöskikt som bildas i säsongspackad snö fortsätter även att utvecklas genom hela vintern. Snölagrens egenskaper förändras beroende på temperaturvariationer, termodynamisk växelverkan med markytan, belastning från ovanliggande snö, regn, med mera. Med accelererande klimatförändringar – särskilt i Arktis – är det viktigt att förstå hur snö och klimat interagerar. I detta projekt analyseras en serie observationer av snöskikt och snöegenskaper, insamlade under mer än 50 år vid Abisko naturvetenskapliga station, jämte en snöpackmodell som ger information om ytterligare egenskaper hos snön. Snödata presenteras både för enskilda säsonger och i långa tidsserier för att fånga upp detaljer såväl som utvecklingen över tid. Där det är möjligt görs jämförelser mellan modelldata och observationer. De fysikaliska processer som ger upphov till förändringar i snön diskuteras och eventuella trender i dataserierna utvärderas. Resultaten visar att snödjup stämmer väl överens mellan modell och observationer. Modellerad snödensitet styrks vid jämförelse med tidiga observationer av densitet som gjorts i Abisko. Snöpackmodellens utdata illustrerar snöns temperaturändringar, perkolation av smältvatten och förtätning (densitetsökning) hos snöskikten. Observationsdata visar förändringar i snöns täthet (hårdhet), snökornens fasthet, kornstorlek samt snöns torrhet. Trendstudier pekar mot att snölagrens täthet ökat och att snöns kornstorlek minskat sedan mätningarna startade.
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Design autonomní sněžné rolby / Design of the autonomous snow groomerVespalec, Arnošt January 2017 (has links)
The subject of this master’s thesis is the design of an autonomous snow groomer intended for the treatment of well-geodetically mapped slopes of ski resorts. Designed design uses an innovative approach to detecting the thickness of a snow cover by an electromagnetic sensor system. The work identifies and presents the concept of solving specific problems of autonomous operation in ski areas, in the form of a design vision. This concept is presented and verified using a parametric model.
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Vliv prostorového rozložení sněhu na průběh povodní / Influence of spatial snow distribution on flood courseKučerová, Dana January 2010 (has links)
For the purpose of hydrological forecasting on mountains' and sub-mountains' rivers is important knowledge of distribution of snow water equivalent in the watershed. Submitted thesis therefore deals with comparison of 9 interpolation methods in terms of quality of their forecasting when predicting snow depth and snow water equivalent in watershed Bystřice (127,6 km2 ), which is situated in the northwest of Bohemia in the Ore mountains. Point data of snow depth and snow water equivalent used in interpolation were sampled during an off- road measuring in 17. 2. 2010 at the 14 snow sampling locations. The interpolation methods were: (1) Thiessen's polygons, (2) inverse distance weighting, (3) global polynomial (4) local polynomial (5) radial basis functions, (6) ordinary kriging, (7) cokriging, (8) residual kriging and (9) orographic interpolation. Independent variable-altitude used in the calculation of snow depth and snow water equivalent was used only in the last three listed methods. Predictive ability of interpolation methods was evaluated by using cross-validation and visual comparison of predicted maps. The best prediction ability was provided by residual kriging and orographic interpolation. The geostatistical methods were next in the order. The method of Thiessen's polygons and inverse distance...
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Framställning av en GIS-metod samt analys av ingående parametrar för att lokalisera representativa delområden av ett avrinningsområde för snödjupsmätningar / Development of a GIS method and analysis of input parameters to locate representative sub-areas of a catchment area for snow depth measurementsKaplin, Jennifer, Leierdahl, Lisa January 2022 (has links)
Vattenkraft är en stor källa till energi i Sverige, främst i de norra delarna av landet. För att få ut maximal potential från vattenkraftverken behövs information om hur mycket vatten eller snö det finns uppströms från kraftverken. Genom att få fram tillförlitliga värden av snömängd är det möjligt att minska osäkerheten i uppskattningarna.Eftersom det är svårt att kartera större avrinningsområden via markbundna observationer, både praktiskt och ekonomiskt, har drönarobservationer utvecklats. För att använda sig av drönare krävs det vetskap om var de ska flygas i för område för att hela avrinningsområdet ska representeras. I projektet tas en modell fram i ArcGIS för att hitta mindre områden inom avrinningsområden som ska vara representativa inom utvalda parametrar. I projektet berörs parametrarna vegetation, höjd, lutningsgrad samt dess riktning.Arbetet för att ta fram en modell som ska underlätta framtida arbete inom och utanför forskningsprojektet DRONES är uppdelat i två delar. Den första delen är att ta fram och granska vilka parametrar som påverkar snödjupet i avrinningsområdet. Den andra delen innefattar arbetet med att skapa en modell i ArcGIS som ska analysera ett avrinningsområde med framtagna parametrar för att hitta mindre områden som representerar det hela.Resultatet från de framtagna modellerna kan tillämpas för att underlätta kartläggningen och snödjupsmätningar i avrinningsområden, vilket kan utnyttjas vid effektivisering av vattenreglering. / Hydropower is a major source of energy in Sweden mainly in the northern parts of the country. To get the maximum potential from the hydropower plants, information is required on how much water or snow there is upstream from the power plants. By obtaining reliable values of the amount of snow, it is possible to reduce the uncertainty in forecasts on spring flood.Due to difficulties in mapping larger catchment areas via ground-level observations, drone observations have been developed. In order to use drone observations, knowledge of where they are to be flown to represent the entire catchment area is required. In this project, a model was developed in ArcGIS to find smaller areas within catchments that are to be representative within selected parameters. The project touches upon the parameters vegetation, height, slope and aspect.The work to develop a model that will facilitate future work within and outside the DRONES research project is divided into two parts. The first part is to analyze which parameters affect the snow depth in the catchment area. The second part consists of creating a model in ArcGIS that will find a smaller area inside a catchment that represents the snow depth for the whole catchment.The results from the developed model can be applied to facilitate the mapping and snow depth measurements in catchment areas, which can be used to streamline water regulation.
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Calibration of Snowmaking Equipment for Efficient Use on Virginia's Smart RoadShea, Edward 16 September 1999 (has links)
Virginia's Smart Road, to be completed by early 2000, is a test bed for numerous research activities including snow and ice control, remote sensor testing, snow removal management, safety and human factors, and vehicle dynamics. An all-weather testing system will feature 75 automated snowmaking towers. In order to provide timely and repeatable weather scenarios, equipment operators will need to understand fully the limitations and capabilities of the snowmaking system.
The research presented herein addresses the hydraulic and hydrologic variables and design methodology to implement efficient snowmaking at a transportation research facility. Design variables include nozzle configuration, water pressure and flowrate, compressed air pressure and flowrate, tower orientation, snow inducer concentration, water and compressed air temperature, and ambient weather conditions. Testing and data collection was performed at the Snow Economics, Inc. research and development site at Seven Springs Mountain Resort in Champion, PA. The results of this work will be used to guide the operators of the Smart Road on the most efficient use of the snowmaking equipment. / Master of Science
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Development of new data fusion techniques for improving snow parameters estimationDe Gregorio, Ludovica 26 November 2019 (has links)
Water stored in snow is a critical contribution to the world’s available freshwater supply and is fundamental to the sustenance of natural ecosystems, agriculture and human societies. The importance of snow for the natural environment and for many socio-economic sectors in several mid‐ to high‐latitude mountain regions around the world, leads scientists to continuously develop new approaches to monitor and study snow and its properties. The need to develop new monitoring methods arises from the limitations of in situ measurements, which are pointwise, only possible in accessible and safe locations and do not allow for a continuous monitoring of the evolution of the snowpack and its characteristics. These limitations have been overcome by the increasingly used methods of remote monitoring with space-borne sensors that allow monitoring the wide spatial and temporal variability of the snowpack. Snow models, based on modeling the physical processes that occur in the snowpack, are an alternative to remote sensing for studying snow characteristics. However, from literature it is evident that both remote sensing and snow models suffer from limitations as well as have significant strengths that it would be worth jointly exploiting to achieve improved snow products. Accordingly, the main objective of this thesis is the development of novel methods for the estimation of snow parameters by exploiting the different properties of remote sensing and snow model data. In particular, the following specific novel contributions are presented in this thesis: i. A novel data fusion technique for improving the snow cover mapping. The proposed method is based on the exploitation of the snow cover maps derived from the AMUNDSEN snow model and the MODIS product together with their quality layer in a decision level fusion approach by mean of a machine learning technique, namely the Support Vector Machine (SVM). ii. A new approach has been developed for improving the snow water equivalent (SWE) product obtained from AMUNDSEN model simulations. The proposed method exploits some auxiliary information from optical remote sensing and from topographic characteristics of the study area in a new approach that differs from the classical data assimilation approaches and is based on the estimation of AMUNDSEN error with respect to the ground data through a k-NN algorithm. The new product has been validated with ground measurement data and by a comparison with MODIS snow cover maps. In a second step, the contribution of information derived from X-band SAR imagery acquired by COSMO-SkyMed constellation has been evaluated, by exploiting simulations from a theoretical model to enlarge the dataset.
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