abstract: Many previous investigators highlight the importance of snowfall to the water supply of the western United States (US). Consequently, the variability of snowpack, snowmelt, and snowfall has been studied extensively. Snow level (the elevation that rainfall transitions to snowfall) directly influences the spatial extent of snowfall and has received little attention in the climate literature. In this study, the relationships between snow level and El Niño-Southern Oscillation (ENSO) as well as Pacific Decadal Oscillation (PDO) are established. The contributions of ENSO/PDO to observed multi-decadal trends are analyzed for the last ~80 years. Snowfall elevations are quantified using three methods: (1) empirically, based on precipitation type from weather stations at a range of elevations; (2) theoretically, from wet-bulb zero heights; (3) theoretically, from measures of thickness and temperature. Statistically significant (p < 0.05) results consistent between the three datasets suggest snow levels are highest during El Niño events. This signal is particularly apparent over the coastal regions and the increased snow levels may be a result of frequent maritime flow into the western US during El Niño events. The El Niño signal weakens with distance from the Pacific Ocean and the Southern Rockies display decreased snow level elevations, likely due to maritime air masses within the mid-latitude cyclones following enhanced meridional flow transitioning to continental air masses. The modulation of these results by PDO suggest that this El Niño signal is amplified (dampened) during the cold (warm) phase of the PDO particularly over Southern California. Additionally, over the coastal states, the La Niña signal during the cold PDO is similar to the general El Niño signal. This PDO signal is likely due to more zonal (meridional) flow throughout winter during the cold (warm) PDO from the weakening (strengthening) of the Aleutian low in the North Pacific. Significant trend results indicate widespread increases in snow level across the western US. These trends span changes in PDO phase and trends with ENSO/PDO variability removed are significantly positive. These results suggest that the wide spread increases in snow level are not well explained by these sea surface temperature oscillations. / Dissertation/Thesis / Ph.D. Geography 2011

Rubber granulates, used at artificial grass pitches, pose a threat to the environment when leaking into the nature. As the granulates leak to the environment through rain water and snow clearances, they can be transported by rivers and later on end up in the marine life. Therefore, reducing the snow clearances to its minimum is of importance. If the snow clearance problem is minimized or even eliminated, this will have a positive impact on the surrounding nature. The object of this project is to propose a method for deciding when to remove snow and automate the information dispersing upon clearing or closing a pitch. This includes finding low powered sensors to measure snow depth, find a machine learning model to predict upcoming snow levels and create an application with a clear and easy-to-use interface to present weather information and disperse information to the responsible persons. Controlled experiments is used to find the models and sensors that are suitable to solve this problem. The sensors are tested on a single snow quality, where ultrasonic and infrared sensors are found suitable. However, fabricated tests for newly fallen snow questioned the possibility of measuring snow depth using the ultrasonic sensor in the general case. Random Forest is presented as the machine learning model that predicts future snow levels with the highest accuracy. From a survey, indications is found that the web application fulfills the intended functionalities, with some improvements suggested.