Distributed Temperatures in the Snow Zone: Spatial Patterns and Innovative Measurement Techniques
Jessica Lundquist, University of Washington
Mountains are spatially complex and sparsely sampled. Temperatures are usually interpolated from distant stations assuming a standard atmospheric lapse rate (decrease of 6.5 per 1000 m elevation gain). However, examination of observed surface temperatures indicates that temperature patterns differ diurnally, synoptically, and seasonally and do not always increase linearly with elevation. This can have profound impacts on snowmelt and runoff forecasts. Fortunately, new technology has become available to monitor temperature in remote locations, such as the Onset Hobo and the Maxim i-button. Approximately 200 of these self-recording sensors have been deployed in Yosemite National Park, California and Niwot Ridge and Rocky Mountain National Park, Colorado over the past several years. Empirical orthogonal functions (EOFs) are used to identify the dominant spatial temperature patterns within each study area and how they vary in time. The spatial patterns of temperature are correlated with topography, such as windward-slope, lee-slope, valley, or ridge. Temporal variations of these surface temperature patterns are correlated with large-scale weather conditions, such winds and pressure patterns. Comparison between study sites allows for generalizations of temperature patterns across space (mapping spatial patterns using topography) and time (mapping temporal variations using large-scale weather parameters), which can be utilized in more sparsely sampled areas. Experimentation with such a large number of sensors also illuminates how to best deploy these small instruments to sample topographically-controlled, versus vegetation-controlled, versus radiation-controlled, temperatures.
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