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    Real-Time Forecasting of Snowfall Using a Neural Network

    Source: Weather and Forecasting:;2007:;volume( 022 ):;issue: 003::page 676
    Author:
    Roebber, Paul J.
    ,
    Butt, Melissa R.
    ,
    Reinke, Sarah J.
    ,
    Grafenauer, Thomas J.
    DOI: 10.1175/WAF1000.1
    Publisher: American Meteorological Society
    Abstract: A set of 53 snowfall reports was collected in real time from the 2004/05 and 2005/06 cold seasons (November?March). Three snowfall-amount forecast methods were tested: neural network, surface-temperature-based 676-USDT table, and climatological snow ratio. Standard verification methods (mean, median, bias, and root-mean-square error) and a new method that places the forecasts in the context of municipal snow removal, and introduces the concept of forecast credibility, are used. Results suggest that the neural network method performs best for individual events, owing in part to the inverse relationship between melted liquid equivalent and snow ratio; hence, the ongoing difficulty of producing accurate forecasts of melted equivalent precipitation (a problem in all seasons) is compensated for rather than amplified when converting to snowfall amounts. This analysis should be extended to a larger selection of reports, which is anticipated in conjunction with efforts currently ongoing at the National Oceanic and Atmospheric Administration?s Hydrometeorological Prediction Center.
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      Real-Time Forecasting of Snowfall Using a Neural Network

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4231137
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    contributor authorRoebber, Paul J.
    contributor authorButt, Melissa R.
    contributor authorReinke, Sarah J.
    contributor authorGrafenauer, Thomas J.
    date accessioned2017-06-09T17:34:44Z
    date available2017-06-09T17:34:44Z
    date copyright2007/06/01
    date issued2007
    identifier issn0882-8156
    identifier otherams-87465.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4231137
    description abstractA set of 53 snowfall reports was collected in real time from the 2004/05 and 2005/06 cold seasons (November?March). Three snowfall-amount forecast methods were tested: neural network, surface-temperature-based 676-USDT table, and climatological snow ratio. Standard verification methods (mean, median, bias, and root-mean-square error) and a new method that places the forecasts in the context of municipal snow removal, and introduces the concept of forecast credibility, are used. Results suggest that the neural network method performs best for individual events, owing in part to the inverse relationship between melted liquid equivalent and snow ratio; hence, the ongoing difficulty of producing accurate forecasts of melted equivalent precipitation (a problem in all seasons) is compensated for rather than amplified when converting to snowfall amounts. This analysis should be extended to a larger selection of reports, which is anticipated in conjunction with efforts currently ongoing at the National Oceanic and Atmospheric Administration?s Hydrometeorological Prediction Center.
    publisherAmerican Meteorological Society
    titleReal-Time Forecasting of Snowfall Using a Neural Network
    typeJournal Paper
    journal volume22
    journal issue3
    journal titleWeather and Forecasting
    identifier doi10.1175/WAF1000.1
    journal fristpage676
    journal lastpage684
    treeWeather and Forecasting:;2007:;volume( 022 ):;issue: 003
    contenttypeFulltext
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