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Application of Snowfall and Wind Statistics to Snow Transport Modeling for Snowdrift Control in Minnesota

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  • a Geophysical Institute, University of Alaska Fairbanks, Fairbanks, Alaska
  • | b Department of Soil, Water, and Climate, University of Minnesota, Saint Paul, Minnesota
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Abstract

Models were utilized to determine the snow accumulation season (SAS) and to quantify windblown snow for the purpose of snowdrift control for locations in Minnesota. The models require mean monthly temperature, snowfall, density of snow, and wind frequency distribution statistics. Temperature and precipitation data were obtained from local cooperative observing sites, and wind data came from Automated Surface Observing System (ASOS)/Automated Weather Observing System (AWOS) sites in the region. The temperature-based algorithm used to define the SAS reveals a geographic variability in the starting and ending dates of the season, which is determined by latitude and elevation. Mean seasonal snowfall shows a geographic distribution that is affected by topography and proximity to Lake Superior. Mean snowfall density also exhibits variability, with lower-density snow events displaced to higher-latitude positions. Seasonal wind frequencies show a strong bimodal distribution with peaks from the northwest and southeast vector direction, with an exception for locations in close proximity to the Lake Superior shoreline. In addition, for western and south-central Minnesota there is a considerably higher frequency of wind speeds above the mean snow transport threshold of 7 m s−1. As such, this area is more conducive to higher potential snow transport totals. Snow relocation coefficients in this area are in the range of 0.4–0.9, and, according to the empirical models used in this analysis, this range implies that actual snow transport is 40%–90% of the total potential in south-central and western areas of the state.

Corresponding author address: Dr. Mark Seeley, Department of Soil, Water, and Climate, Rm. 439, Borlaug Hall, University of Minnesota, St. Paul, MN 55108. mseeley@soils.umn.edu

Abstract

Models were utilized to determine the snow accumulation season (SAS) and to quantify windblown snow for the purpose of snowdrift control for locations in Minnesota. The models require mean monthly temperature, snowfall, density of snow, and wind frequency distribution statistics. Temperature and precipitation data were obtained from local cooperative observing sites, and wind data came from Automated Surface Observing System (ASOS)/Automated Weather Observing System (AWOS) sites in the region. The temperature-based algorithm used to define the SAS reveals a geographic variability in the starting and ending dates of the season, which is determined by latitude and elevation. Mean seasonal snowfall shows a geographic distribution that is affected by topography and proximity to Lake Superior. Mean snowfall density also exhibits variability, with lower-density snow events displaced to higher-latitude positions. Seasonal wind frequencies show a strong bimodal distribution with peaks from the northwest and southeast vector direction, with an exception for locations in close proximity to the Lake Superior shoreline. In addition, for western and south-central Minnesota there is a considerably higher frequency of wind speeds above the mean snow transport threshold of 7 m s−1. As such, this area is more conducive to higher potential snow transport totals. Snow relocation coefficients in this area are in the range of 0.4–0.9, and, according to the empirical models used in this analysis, this range implies that actual snow transport is 40%–90% of the total potential in south-central and western areas of the state.

Corresponding author address: Dr. Mark Seeley, Department of Soil, Water, and Climate, Rm. 439, Borlaug Hall, University of Minnesota, St. Paul, MN 55108. mseeley@soils.umn.edu

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