An Empirical Blowing Snow Forecast Technique for the Canadian Arctic and the Prairie Provinces

David G. Baggaley Prairie and Arctic Storm Prediction Centre, Meteorological Service of Canada, Winnipeg, Manitoba, Canada

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John M. Hanesiak Centre for Earth Observation Science, Faculty of Environment, University of Manitoba, Winnipeg, Manitoba, Canada

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Abstract

Blowing snow has a major impact on transportation and public safety. The goal of this study is to provide an operational technique for forecasting high-impact blowing snow on the Canadian arctic and the Prairie provinces using historical meteorological data. The focus is to provide some guidance as to the probability of reduced visibilities (e.g., less than 1 km) in blowing snow given a forecast wind speed and direction. The wind character associated with blowing snow was examined using a large database consisting of up to 40 yr of hourly observations at 15 locations in the Prairie provinces and at 17 locations in the arctic. Instances of blowing snow were divided into cases with and without concurrent falling snow. The latter group was subdivided by the time since the last snowfall in an attempt to account for aging processes of the snowpack. An empirical scheme was developed that could discriminate conditions that produce significantly reduced visibility in blowing snow using wind speed, air temperature, and time since last snowfall as predictors. This process was evaluated using actual hourly observations to compute the probability of detection, false alarm ratio, credibility, and critical success index. A critical success index as high as 66% was achieved. This technique can be used to give an objective first guess of the likelihood of high-impact blowing snow using common forecast parameters.

Corresponding author address: David G. Baggaley, Prairie and Arctic Storm Prediction Centre (PASPC), Meteorological Service to Canada, 123 Main Street, Suite 150, Winnipeg, MB R3C 4W2, Canada. Email: david.baggaley2@ec.gc.ca

Abstract

Blowing snow has a major impact on transportation and public safety. The goal of this study is to provide an operational technique for forecasting high-impact blowing snow on the Canadian arctic and the Prairie provinces using historical meteorological data. The focus is to provide some guidance as to the probability of reduced visibilities (e.g., less than 1 km) in blowing snow given a forecast wind speed and direction. The wind character associated with blowing snow was examined using a large database consisting of up to 40 yr of hourly observations at 15 locations in the Prairie provinces and at 17 locations in the arctic. Instances of blowing snow were divided into cases with and without concurrent falling snow. The latter group was subdivided by the time since the last snowfall in an attempt to account for aging processes of the snowpack. An empirical scheme was developed that could discriminate conditions that produce significantly reduced visibility in blowing snow using wind speed, air temperature, and time since last snowfall as predictors. This process was evaluated using actual hourly observations to compute the probability of detection, false alarm ratio, credibility, and critical success index. A critical success index as high as 66% was achieved. This technique can be used to give an objective first guess of the likelihood of high-impact blowing snow using common forecast parameters.

Corresponding author address: David G. Baggaley, Prairie and Arctic Storm Prediction Centre (PASPC), Meteorological Service to Canada, 123 Main Street, Suite 150, Winnipeg, MB R3C 4W2, Canada. Email: david.baggaley2@ec.gc.ca

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  • Atmospheric Environment Service, 1977: Manual of Surface Weather Observations (MANOBS). 7th ed. Atmospheric Environment, 322 pp.

  • Davenport, A. G., 1960: Wind loads on structures. Division of Building Research, National Research Council, Canada, NRCC-5576, 60 pp.

  • Dery, S. J., and Yau M. K. , 2001: Simulation of an arctic ground blizzard using a coupled blowing snow–atmosphere model. J. Hydrometeor., 2 , 579598.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elliot, D. L., 1986: Wind Energy Resource Atlas of the United States. Solar Technical information Program, Solar Energy Research Institute Rep. DOE/CH 10093-4, 210 pp.

    • Search Google Scholar
    • Export Citation
  • Hanesiak, J. M., Barber D. G. , and Flato G. M. , 1999: The role of diurnal processes in the seasonal evolution of sea ice and its snow cover. J. Geophys. Res., 104 , C6,. 1359313604.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hanesiak, J. M., Fisico T. , and Carriere E. , 2003: Climatology of adverse weather events in the Canadian arctic. Centre for Earth Observation Science Tech. Rep. CEOS-Tech-2003-2, 3540 pp. [Available from Faculty of Environment, University of Manitoba, Winnipeg, MB R32 2N2, Canada.].

  • Jordan, R., Andreas E. A. , and Makshtas A. P. , 1999: Heat budget of snow-covered sea ice at North Pole 4. J. Geophys. Res., 104 , 77857806.

  • Lawson, B., 2002: Trends in blizzards at selected locations in the Canadian prairies. J. Natural Hazards, 29 , 2,. 123138.

  • Li, L., and Pomeroy J. W. , 1997a: Estimates of threshold wind speeds for snow transport using meteorological data. J. Appl. Meteor., 36 , 205213.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, L., and Pomeroy J. W. , 1997b: Probability of occurrence of blowing snow. J. Geophys. Res., 102 , 2195521964.

  • Naiman, R., Rosenfield R. , and Zirkel G. , 1977: Understanding Statistics. 2d ed. McGraw-Hill, 307 pp.

  • Phillips, D. W., 1990: The Climates of Canada. Canadian Government Publishing Centre, 176 pp.

  • Pomeroy, J. W., and Male D. H. , 1988: Optical properties of blowing snow. J. Glaciol., 34 , 116,. 310.

  • Rasmussen, R. M., Vivekanandan J. , and Cole J. , 1998: The estimation of snowfall rate using visibility. J. Appl. Meteor., 38 , 15421563.

    • Search Google Scholar
    • Export Citation
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