Improving Snowfall Forecasting by Accounting for the Climatological Variability of Snow Density

Eric C. Ware Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York

Search for other papers by Eric C. Ware in
Current site
Google Scholar
PubMed
Close
,
David M. Schultz Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by David M. Schultz in
Current site
Google Scholar
PubMed
Close
,
Harold E. Brooks NOAA/National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Harold E. Brooks in
Current site
Google Scholar
PubMed
Close
,
Paul J. Roebber Atmospheric Science Group, Department of Mathematical Sciences, University of Wisconsin—Milwaukee, Milwaukee, Wisconsin

Search for other papers by Paul J. Roebber in
Current site
Google Scholar
PubMed
Close
, and
Sara L. Bruening Atmospheric Science Group, Department of Mathematical Sciences, University of Wisconsin—Milwaukee, Milwaukee, Wisconsin

Search for other papers by Sara L. Bruening in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Accurately forecasting snowfall is a challenge. In particular, one poorly understood component of snowfall forecasting is determining the snow ratio. The snow ratio is the ratio of snowfall to liquid equivalent and is inversely proportional to the snow density. In a previous paper, an artificial neural network was developed to predict snow ratios probabilistically in three classes: heavy (1:1 < ratio < 9:1), average (9:1 ≤ ratio ≤ 15:1), and light (ratio > 15:1). A Web-based application for the probabilistic prediction of snow ratio in these three classes based on operational forecast model soundings and the neural network is now available. The goal of this paper is to explore the statistical characteristics of the snow ratio to determine how temperature, liquid equivalent, and wind speed can be used to provide additional guidance (quantitative, wherever possible) for forecasting snowfall, especially for extreme values of snow ratio. Snow ratio tends to increase as the low-level (surface to roughly 850 mb) temperature decreases. For example, mean low-level temperatures greater than −2.7°C rarely (less than 5% of the time) produce snow ratios greater than 25:1, whereas mean low-level temperatures less than −10.1°C rarely produce snow ratios less than 10:1. Snow ratio tends to increase strongly as the liquid equivalent decreases, leading to a nomogram for probabilistic forecasting snowfall, given a forecasted value of liquid equivalent. For example, liquid equivalent amounts 2.8–4.1 mm (0.11–0.16 in.) rarely produce snow ratios less than 14:1, and liquid equivalent amounts greater than 11.2 mm (0.44 in.) rarely produce snow ratios greater than 26:1. The surface wind speed plays a minor role by decreasing snow ratio with increasing wind speed. Although previous research has shown simple relationships to determine the snow ratio are difficult to obtain, this note helps to clarify some situations where such relationships are possible.

Corresponding author address: Dr. David M. Schultz, NOAA/National Severe Storms Laboratory, 1313 Halley Circle, Norman, OK 73069. Email: david.schultz@noaa.gov

Abstract

Accurately forecasting snowfall is a challenge. In particular, one poorly understood component of snowfall forecasting is determining the snow ratio. The snow ratio is the ratio of snowfall to liquid equivalent and is inversely proportional to the snow density. In a previous paper, an artificial neural network was developed to predict snow ratios probabilistically in three classes: heavy (1:1 < ratio < 9:1), average (9:1 ≤ ratio ≤ 15:1), and light (ratio > 15:1). A Web-based application for the probabilistic prediction of snow ratio in these three classes based on operational forecast model soundings and the neural network is now available. The goal of this paper is to explore the statistical characteristics of the snow ratio to determine how temperature, liquid equivalent, and wind speed can be used to provide additional guidance (quantitative, wherever possible) for forecasting snowfall, especially for extreme values of snow ratio. Snow ratio tends to increase as the low-level (surface to roughly 850 mb) temperature decreases. For example, mean low-level temperatures greater than −2.7°C rarely (less than 5% of the time) produce snow ratios greater than 25:1, whereas mean low-level temperatures less than −10.1°C rarely produce snow ratios less than 10:1. Snow ratio tends to increase strongly as the liquid equivalent decreases, leading to a nomogram for probabilistic forecasting snowfall, given a forecasted value of liquid equivalent. For example, liquid equivalent amounts 2.8–4.1 mm (0.11–0.16 in.) rarely produce snow ratios less than 14:1, and liquid equivalent amounts greater than 11.2 mm (0.44 in.) rarely produce snow ratios greater than 26:1. The surface wind speed plays a minor role by decreasing snow ratio with increasing wind speed. Although previous research has shown simple relationships to determine the snow ratio are difficult to obtain, this note helps to clarify some situations where such relationships are possible.

Corresponding author address: Dr. David M. Schultz, NOAA/National Severe Storms Laboratory, 1313 Halley Circle, Norman, OK 73069. Email: david.schultz@noaa.gov

Save
  • Baxter, M. A., Graves C. E. , and Moore J. T. , 2005: A climatology of snow-to-liquid ratio for the contiguous United States. Wea. Forecasting, 20 , 729744.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Currie, B. W., 1947: Water content of snow in cold climates. Bull. Amer. Meteor. Soc., 28 , 150151.

  • Doesken, N. J., and Judson A. , 1997: The Snow Booklet: A Guide to the Science, Climatology, and Measurement of Snow in the United States. Dept. of Atmospheric Science, Colorado State University, 86 pp.

    • Search Google Scholar
    • Export Citation
  • Fukuta, N., and Takahashi T. , 1999: The growth of atmospheric ice crystals: A summary of findings in vertical supercooled cloud tunnel studies. J. Atmos. Sci., 56 , 19631979.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goodison, B. E., 1978: Accuracy of Canadian snow gage measurements. J. Appl. Meteor., 17 , 15421548.

  • Gray, D. M., and Male D. H. , 1981: Handbook of Snow: Principles, Processes, Management and Use. Pergammon Press, 776 pp.

  • Groisman, P. Ya, and Easterling D. R. , 1994: Variability and trends of total precipitation and snowfall over the United States and Canada. J. Climate, 7 , 184205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., 1986: Ice particle evolution in the anvil of a severe thunderstorm during CCOPE. J. Atmos. Sci., 43 , 24632478.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hobbs, P. V., Chang S. , and Locatelli J. D. , 1974: The dimensions and aggregation of ice crystals in natural clouds. J. Geophys. Res., 79 , 21992206.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Judson, A., and Doesken N. , 2000: Density of freshly fallen snow in the central Rocky Mountains. Bull. Amer. Meteor. Soc., 81 , 15771587.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • LaChapelle, E. R., 1962: The density distribution of new snow. Project F, Progress Rep. 2, Alta Avalanche Study Center, Wasatch National Forest, USDA Forest Service, Salt Lake City, UT, 13 pp.

  • Magono, C., and Lee C. W. , 1966: Meteorological classification of natural snow crystals. J. Fac. Sci., Hokkaido Univ., Ser. 7, II, 321–335.

    • Search Google Scholar
    • Export Citation
  • Power, B. A., Summers P. W. , and d'Avignon J. , 1964: Snow crystal forms and riming effects as related to snowfall density and general storm conditions. J. Atmos. Sci., 21 , 300305.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmussen, R. M., Vivekanandan J. , Cole J. , Myers B. , and Masters C. , 1999: The estimation of snowfall rate using visibility. J. Appl. Meteor., 18 , 15421563.

    • Search Google Scholar
    • Export Citation
  • Roebber, P. J., Bruening S. L. , Schultz D. M. , and Cortinas J. V. Jr., 2003: Improving snowfall forecasting by diagnosing snow density. Wea. Forecasting, 18 , 264287.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ryan, B. F., Wishart E. R. , and Shaw D. E. , 1976: The growth rates and densities of ice crystals between −3°C and −21°C. J. Atmos. Sci., 33 , 842850.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, B. E., and Govett M. , 1992: A hydrostatically consistent North American radiosonde data base at the Forecast Systems Laboratory, 1946–present. NOAA Tech. Memo. ERL FSL-4, 81 pp. [Available from NOAA/FSL, 325 Broadway, Boulder, CO 80303.].

  • Super, A. B., and Holroyd E. W. III, 1997: Snow accumulation algorithm for the WSR-88D radar: Second annual report. Bureau of Reclamation Tech. Rep. R-97-05, U.S. Dept. of Interior, Denver, CO, 77 pp. [Available from National Technical Information Service, 5285 Port Royal Rd., Springfield, VA 22161.].

  • Yang, D., Goodison B. E. , Metcalfe J. R. , Golubev V. S. , Bates R. , Pangburn T. , and Hanson C. L. , 1998: Accuracy of NWS 8” standard nonrecording precipitation gauge: Results and application of WMO intercomparison. J. Atmos. Oceanic Technol., 15 , 5468.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 639 250 97
PDF Downloads 488 144 17