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,
  15°C]-95%.
  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.
  iv
  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

  • Prediction Interval
  • Uncertainty Modeling
  • Statistical post-processing
  • Data clustering
  • Probabilistic forecast
  • Time series analysis
  • Interval Forecast
  • Numerical Weather Prediction
  • Quantile Regression

• Graduation date

  Spring 2014

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/R3P55DP9Z

• 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.