Improving hub-height wind forecasts in complex terrain

• Main Author: Siuta, David
• Language: English
• Published: University of British Columbia 2017
• Online Access: http://hdl.handle.net/2429/61055

Wind-speed forecasts from numerical-weather-prediction (NWP) models are important for daily wind-resource generation planning. However, NWP models are imperfect. The ability of energy planners to efficiently manage resources is a function of the accuracy of deterministic wind forecasts and of the associated probability estimates of forecast uncertainty. As the amount of energy generated from wind increases to significant levels, improving forecast accuracy and representation of forecast uncertainty is a key area of active research. This dissertation advances wind forecasting over regions of complex topography using the Weather Research and Forecasting (WRF) model. The optimal WRF-model configuration is a function of planetary-boundary-layer (PBL) physics, grid length, and initial-condition choice. The first component of this work determines which of these three factors most influences forecast accuracy over complex terrain. The two largest factors influencing forecast accuracy are the PBL-physics scheme and the grid length, with the dominant factor being a function of location, season, and time of day. The second component of the research addresses the need for probability-based forecast information, which is only recently being used within the industry. Wind forecasts from an ensemble using eight PBL schemes, three grid lengths, and two initial-conditions sources are converted into probability models that are then evaluated. Using the full, empirical ensemble distribution produces uncalibrated probabilistic forecasts. Prescribing a Gaussian probability distribution based on statistical moments of a past training dataset results in calibrated and sharp probabilistic forecasts. Such a method is also computationally cheap. The final aspect of this study evaluates the role of boundary-layer static stability on forecast performance. Traditionally, empirical surface-layer similarity theory has been used to relate surface fluxes of heat, momentum, and moisture to vertical profiles of temperature and wind. To evaluate and improve surface-layer similarity theories over mountain ridges, a year-long field campaign of temperature and wind measurements was conducted at wind farms in British Columbia. New empirical equations for complex terrain are proposed based on the field data, and found to perform well at an independent test location. === Science, Faculty of === Earth, Ocean and Atmospheric Sciences, Department of === Graduate