Data are presented from an automatic weather station on the Brintnell-Bologna Icefield that operated from August 2014 to August 2016 in Nahanni National Park Reserve. This location is notable for being the northernmost mass balance alpine study location of the federal government’s glaciology program (NRCan/GSC). The link between atmospheric forcing at the synoptic scale and response at the glacier surface has been shown to be strongly dependent on continentality and latitude. In this region, however, many aspects of the physical processes controlling the interaction between atmospheric forcing and snowpack response are virtually unknown, especially at the daily to hourly timescale. The character of snowfalls during the accumulation seasons for this icefield are investigated using high resolution time series from two acoustic snow depth sensors and other relevant meteorological parameters. It is found that the most drastic changes in snow depth occur from infrequent large snowfalls. Using an adaption of an Environment Canada snow depth algorithm, snowfall events are identified and their timing is quantified based on a system of thresholds, running averages and ratios between the snow depth sensors. Synoptic conditions are examined using meteorological reanalysis data and trajectory analysis to determine the moisture origin and pathway. / Graduate

The winter urban heat island (UHI) has been suggested to reduce snowfall downwind of city centers due to localized energy and moisture flux variations, but previous research lacks spatial detail since it is primarily based on sparse surface observations. This project utilizes high-resolution radar data for twelve snow-only events, occurring from 1995-2008 and passing over the Minneapolis – St. Paul (MSP) urban area, to quantify the change in radar reflectivity values downwind of the city. Results show that five of the twelve snowfall events evaluated for the MSP urban area did not significantly decrease in summed decibel (dBZ) values downwind of the city center, and four of these events occurred on a day with a zero to positive urban-rural temperature gradient. Analysis of related atmospheric variables on these days suggest that atmospheric instability and convergence may play a critical role in urban snowfall modification.

No description available.

Correlation tests were run on yearly snowfall and tornado activity data collected between water-years 1965/66 and 2010/11. Snowfall activity was evaluated using two separate measurements, which included snowfall days and daily snow depth. Tornado activity was measured through tornado days and total yearly tornado occurrences. Tornado days were defined as a 24-h period (0600–0600 UTC) during which either one or more tornadoes occurred within a chosen southern region boundary. Correlation tests revealed an absence of any mutual relationship between the snowfall and tornado activity. Three prominent teleconnections (ENSO, NAO, & AO), 6-month (Oct-Dec and Feb-Apr) and 3-month means (Oct-May) were also analyzed to reveal possible correlations with the tornado and snowfall activity. Significant negative correlations were found between ONI × tornado days; ONI × tornado totals; NAOI × snowfall days; NAOI × snow depth; AOI × snowfall days; and AOI × snow depth

Precipitation phase is fundamental to a catchment’s hydrological response to precipitation events in cold regions and is especially variable over time and space in complex topography. Phase is controlled by the microphysics of the falling hydrometeor, but microphysical calculations require detailed atmospheric information that is often unavailable and lacking from hydrological analyses. In hydrology, there have been many methods developed to estimate phase, but most are regionally calibrated and many depend on air temperature (Ta) and use daily time steps. Phase is not only related to Ta, but to other meteorological variables such as humidity. In addition, precipitation events are dynamic, adding uncertainties to the use of daily indices to estimate phase. To better predict precipitation phase with respect to meteorological conditions, the combined mass and energy balance of a falling hydrometeor was calculated and used to develop a model to estimate precipitation phase. Precipitation phase and meteorological data were observed at multiple elevations in a small Canadian Rockies catchment, Marmot Creek Research Basin, at 15-minute intervals over several years to develop and test the model. The mass and energy balance model was compared to other methods over varying time scales, seasons, elevations and topographic exposures. The results indicate that the psychrometric energy balance model performs much better than Ta methods and that this improvement increases as the calculation time interval decreases. The uncertainty that differing phase methods introduce to hydrological process estimation was assessed with the Cold Regions Hydrological Model (CRHM). The rainfall/total precipitation ratio, runoff, discharge and snowpack accumulation were calculated using a single and a double Ta threshold method and the proposed physically based mass and energy balance model. Intercomparison of the hydrological responses of the methods highlighted differences between Ta based and psychrometric approaches. Uncertainty of hydrological processes, as established by simulating a wide range of Ta methods, reached up to 20% for rain ratio, 1.5 mm for mean daily runoff, 0.4 mm for mean daily discharge and 160 mm of peak snow water equivalent. The range of Ta methods showed that snowcover duration, snow free date and peak discharge date could vary by up to 36, 26 and 10 days respectively. The greatest hydrological uncertainty due to precipitation phase methods was found at sub-alpine and sub-arctic headwater basins and the least uncertainty was found at a small prairie basin.

South Africa is located in the sub tropics with an elevated plateau which is located approximately 1500 m above mean sea level (a.m.s.l). Every year, snow occurs on the mountains of Lesotho, but on occasions this snow descends to lower elevations which impacts on the livelihood of people. Severe weather originating from extra-tropical weather systems has been well documented in South Africa and yet very little research has been done to predict significant snowfall from these weather systems. The main aim of this research is to identify those weather systems responsible for snow and to understand the processes causing snow to form when these systems occur. A comprehensive database of significant snowfall events is supplied from 1981 to 2011. The database is subjectively classified into characteristic synoptic patterns. The snow cases are then objectively classified using self-organising maps (SOMs) to obtain synoptic configurations most typically associated with significant snowfall over South Africa. Case studies which aim to explain the synoptic conditions, formation mechanisms as well as critical surface temperature and relative humidity during snowfall events are described. This is done by analysing each case study with respect to synoptic circulations, surface observations, atmospheric soundings, satellite imagery as well as atmospheric thickness. Conclusions are drawn and critical threshold values of atmospheric thickness, surface temperature and humidity are identified when snowfall occurs. A methodical snow forecasting decision tree is devised. It takes the synoptic classification of circulation patterns during significant snowfall, atmospheric thickness, height of the freezing level, surface temperature, and relative humidity into account. This process is explained by case studies. It is recommended that results from this dissertation are made available to weather forecasters in South Africa and that the results are implemented in the operational forecasting environment. Further case study investigations are suggested, taking the mesoscale processes effects into account. / Dissertation (MSc)--University of Pretoria, 2013. / am2013 / Geography, Geoinformatics and Meteorology / MSc / Unrestricted

No description available.

The precipitation falling as rain or snow has different impact on regional water resources and their annual distribution. Shift from solid to liquid form of precipitation following the increase of the surface air temperatures could be important because such change could influence the timing of spring runoff and cause water scarcity in summer. In this study, the spatial and temporal variations of ratio of snowfall to total precipitation (Sf), mean air temperature, day of year of melt-out and winter and spring runoff is analysed. Data were examined for 11 meteorological and 6 hydrological stations in the mountains catchment in Czechia for November-April 1965-2014. Data were analyzed using the Mann-Kendall trend test. Major results show that Sf has been decreasing strongly throughout the whole examined area, with the strongest decrease in the foothill area of the northern mountains of Czechia. Stronger decrease is observed in lower elevations, at the stations with meant air temperature close to melt temperature. Strongest decrease was observed in March and the weakest decrease was observed in December and April, The significant decreases in Sf are associated with large increase in mean winter air temperatures. Due to the increasing mean air temperature in the cold season, the total rainfall is...

Les extrêmes neigeux sont parmi les risques naturels les plus dangereux dans les régions montagneuses. Les processus max-stables, qui relient statistique des valeurs extrêmes et géostatistique, offrent un cadre approprié pour les étudier. Deux questions importantes concernant la dépendance spatiale des extrêmes sont traitées dans cette thèse à travers les cas des chutes et des hauteurs de neige dans les Alpes françaises : la sélection de modèle et la non-stationnarité temporelle. Nous utilisons pour cela deux jeux de données de maxima hivernaux de chutes de neige (90 stations de 1958 à 2013) et de hauteurs de neige (82 stations de 1970 à 2013). Nous décrivons d'abord une procédure de validation-croisée appropriée pour évaluer les capacités des processus max-stables à capturer la structure de dépendance des extrêmes spatiaux. Nous mettons en exergue trois processus max-stables pour leur aptitude à modéliser la dépendance spatiale des chutes de neige extrêmes : les processus de Brown-Resnick, géométrique gaussien et extrémal-t. Les performances de ces trois modèles sont extrêmement similaires, quel que soit le nombre de stations ou d'années. Ensuite, nous présentons une approche par fenêtre glissante pour évaluer l'évolution temporelle de la dépendance des extrêmes spatiaux. Nous montrons ainsi que les chutes de neige extrêmes ont tendance à être de moins en moins dépendantes spatialement. Nous montrons que cela est dû à une augmentation de la température provoquant une baisse du ratio neige/pluie. Il existe aussi un effet d'intensité avec des extrêmes moins dépendants à cause d'une baisse du cumul hivernal de chutes de neige. Enfin, nous présentons la première utilisation de processus max-stables avec des tendances temporelles dans la structure de dépendance spatiale. Cette approche est appliquée aux maxima de hauteurs de neige modélisés par un processus de Brown-Resnick. Nous montrons que leur dépendance spatiale est impactée par le changement climatique d'une manière similaire que celle des chutes de neige extrêmes. / Extreme snowfall and extreme snow depths are among the most dangerous hazards in the mountainous regions. Max-stable processes, which connect extreme value statistics and geostatistics by modeling the spatial dependence of extremes, offer a suitable framework to deal with. Two challenging issues concerning spatial dependence of extremes are broached in this thesis through the examples of snowfall and snow depths in the French Alps: model selection and temporal nonstationarity. We process two winter maxima data sets of 3-day snowfall (90 stations from 1958 to 2013) and snow depths (82 stations from 1970 to 2013). First, we introduce a leave-two-out cross-validation procedure appropriate for evaluating the predictive ability of max-stable processes to model the dependence structure of spatial extremes. We compare five of the most commonly used max-stable processes, using as a case study the snowfall maxima data set. This approach allows us to show that the extremal-t, geometric Gaussian and Brown-Resnick processes are able to represent as well the structure of dependence of the data, regardless of the number of stations or years. Then, we show, using a data-based approach allowing to make minimal modeling assumptions, that snowfall extremes tended to become less spatially dependent over time, with the dependence range reduced roughly by half during the study period. We demonstrate that this is attributable at first to the increase in temperature and its major control on the snow/rain partitioning. A magnitude effect, with less dependent extremes due to a decrease in winter cumulated snowfall, also exists. Finally, we tackle the first-ever use of max-stable processes with temporal trends in the spatial dependence structure. This approach is applied to snow depth winter maxima modeled by a Brown-Resnick process. We show that the spatial dependence of extreme snow depths is impacted by climate change in a similar way to that has been observed for extreme snowfall.

From the Proceedings of the 1973 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - May 4-5, 1973, Tucson, Arizona / A preliminary assessment and ranking of the relative significance of five processes that may contribute to snow removal from ponderosa pine forest canopies was made, including wind erosion of canopy snow, snowslide from the canopy, stemflow, vapor transport from melt water, and vapor transport of canopy snow. The first three represent delayed delivery rather than net water loss. A snow load index was obtained through use of time lapse photography of the study site canopy, while incoming solar radiation and atmospheric processes were monitored. The snow load index was expressed as a ratio of forest canopy area covered with snow to the total canopy area. Results obtained over a 4-day period following a six-hour snowstorm showed that snow removal by snowslide and wind erosion was of significant importance, while vapor transport of melt water and canopy snow, stemflow, and dripping of melt water was of comparatively minor importance.