Modeling Uncertainty of Numerical Weather Predictions Using Learning Methods

  • Author / Creator
    Zarnani, Ashkan
  • Weather forecasting is one of the most vital tasks in many applications ranging from
    severe weather hazard systems to energy production. Numerical weather prediction
    (NWP) systems are commonly used state-of-the-art atmospheric models that provide
    point forecasts as deterministic predictions arranged on a three-dimensional grid.
    However, there is always some level of error and uncertainty in the forecasts due to
    inaccuracies of initial conditions, the chaotic nature of weather, etc. Such uncertainty
    information is crucial in decision making and optimization processes involved in many
    applications. A common representation of forecast uncertainty is a Prediction Interval
    (PI) that determines a minima, maxima and confidence level for each forecast, e.g. [2°C,
    In this study, we investigate various methods that can model the uncertainty of NWP
    forecasts and provide PIs for the forecasts accordingly. In particular, we are interested in
    analyzing the historical performance of the NWP system as a valuable source for
    uncertainty modeling. Three different classes of methods are developed and applied for
    this problem. First, various clustering algorithms (including fuzzy c-means) are employed
    in concert with fitting appropriate probability distributions to obtain statistical models
    that can dynamically provide PIs depending on the forecast context. Second, a range of
    quantile regression methods (including kernel quantile regression) are studied that can
    directly model the PI boundaries as a function of influential features. In the third class,
    we focus on various time series modeling approaches including heteroscedasticity
    modeling methods that can provide forecasts of conditional mean and conditional
    variance of the target for any forecast horizon.
    All presented PI computation methods are empirically evaluated using a developed
    comprehensive verification framework in a set of experiments involving real-world data
    sets of NWP forecasts and observations. A key component is proposed in the evaluation
    process that would lead to a considerably more reliable judgment. Results show that PIs
    obtained by the ARIMA-GARCH model (for up to 6-hour-ahead forecasts) and Spline
    Quantile Regression (for longer leads) provide interval forecasts with satisfactory
    reliability and significantly better skill. This can lead to improvements in forecast value
    for many systems that rely on the NWP forecasts.

  • Subjects / Keywords
  • Graduation date
    Spring 2014
  • Type of Item
  • Degree
    Doctor of Philosophy
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
  • Specialization
    • Software Engineering and Intelligent Systems
  • Supervisor / co-supervisor and their department(s)
  • Examining committee members and their departments
    • Fedosejevs, Robert (ECE)
    • Reformat, Marek (ECE)
    • Tavakoli, Mahdi (ECE)
    • Robinson, Aminah (Department of Civil and Environmental Engineering)
    • Hsieh, William (Department of Earth, Ocean and Atmospheric Sciences, UBC)