• Login
    View Item 
    •   SMARTech Home
    • Georgia Tech Theses and Dissertations
    • Georgia Tech Theses and Dissertations
    • View Item
    •   SMARTech Home
    • Georgia Tech Theses and Dissertations
    • Georgia Tech Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Characteristics of ensemble forecasting systems for southeastern snowfall events

    Thumbnail
    View/Open
    HAYNES-THESIS-2017.pdf (3.810Mb)
    Date
    2017-07-27
    Author
    Haynes, Robert D.
    Metadata
    Show full item record
    Abstract
    The use of ensemble forecasting has burgeoned with the advent of greater technological resources. Forecasters now have available to them a range of possible forecast outcomes that can be utilized to understand forecast biases and to convey the most likely or worst case scenarios. However, ensemble forecasting systems must be used responsibly. If an ensemble is under-dispersive or heavily-biased, the observations may fall outside the ensemble, in which case it provides little useful information. Examining the behavior of the European Center’s Ensemble Prediction System (EC EPS), these characteristics are analyzed in terms of snow water liquid equivalent forecasts for the 2010-2015 DJF period. Selected for the advantages of a large member count, a linear regression model is created using bins populated by ensemble variances and error variances of the EC EPS, ordered by ascending ensemble variance, to calibrate the ensembles to provide a 1:1 prediction of the range of errors from the ensemble mean forecast. Training statistics indicate the model based on ensemble variance explains more than 90% of the error variance at all lead times. However, in-depth analysis of two out-of-sample, disparate winter weather events demonstrate how the linear regression model relies too heavily on the ensemble spread and can over-forecast or under-forecast depending on how large the ensemble spread is relative to the 2010-2015 DJF period. All things considered, the application of a linear model relating ensemble variance to error variance provides a promising means to adjust an ensemble system’s bias in estimating the range of possible outcomes for snowfall in the Southeastern US (SE US).
    URI
    http://hdl.handle.net/1853/58747
    Collections
    • Georgia Tech Theses and Dissertations [23877]
    • School of Earth and Atmospheric Sciences Theses and Dissertations [543]

    Browse

    All of SMARTechCommunities & CollectionsDatesAuthorsTitlesSubjectsTypesThis CollectionDatesAuthorsTitlesSubjectsTypes

    My SMARTech

    Login

    Statistics

    View Usage StatisticsView Google Analytics Statistics
    facebook instagram twitter youtube
    • My Account
    • Contact us
    • Directory
    • Campus Map
    • Support/Give
    • Library Accessibility
      • About SMARTech
      • SMARTech Terms of Use
    Georgia Tech Library266 4th Street NW, Atlanta, GA 30332
    404.894.4500
    • Emergency Information
    • Legal and Privacy Information
    • Human Trafficking Notice
    • Accessibility
    • Accountability
    • Accreditation
    • Employment
    © 2020 Georgia Institute of Technology