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

    Source: Weather and Forecasting:;2006:;volume( 021 ):;issue: 001::page 94
    Author:
    Ware, Eric C.
    ,
    Schultz, David M.
    ,
    Brooks, Harold E.
    ,
    Roebber, Paul J.
    ,
    Bruening, Sara L.
    DOI: 10.1175/WAF903.1
    Publisher: American Meteorological Society
    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.
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      Improving Snowfall Forecasting by Accounting for the Climatological Variability of Snow Density

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4231275
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    contributor authorWare, Eric C.
    contributor authorSchultz, David M.
    contributor authorBrooks, Harold E.
    contributor authorRoebber, Paul J.
    contributor authorBruening, Sara L.
    date accessioned2017-06-09T17:35:04Z
    date available2017-06-09T17:35:04Z
    date copyright2006/02/01
    date issued2006
    identifier issn0882-8156
    identifier otherams-87590.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4231275
    description abstractAccurately 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.
    publisherAmerican Meteorological Society
    titleImproving Snowfall Forecasting by Accounting for the Climatological Variability of Snow Density
    typeJournal Paper
    journal volume21
    journal issue1
    journal titleWeather and Forecasting
    identifier doi10.1175/WAF903.1
    journal fristpage94
    journal lastpage103
    treeWeather and Forecasting:;2006:;volume( 021 ):;issue: 001
    contenttypeFulltext
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