A WRF Ensemble for Improved Wind Speed Forecasts at Turbine Height

Thumbnail Image
Date
2013-01-01
Authors
Deppe, Adam
Gallus, William
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract

The Weather Research and Forecasting Model (WRF) with 10-km horizontal grid spacing was used to explore improvements in wind speed forecasts at a typical wind turbine hub height (80 m). An ensemble consisting of WRF model simulations with different planetary boundary layer (PBL) schemes showed little spread among the individual ensemble members for forecasting wind speed. A second configuration using three random perturbations of the Global Forecast System model produced more spread in the wind speed forecasts, but the ensemble mean possessed a higher mean absolute error (MAE). A third ensemble of different initialization times showed larger model spread, but model MAE was not compromised. In addition, postprocessing techniques such as training of the model for the day 2 forecast based on day 1 results and bias correction based on observed wind direction are examined. Ramp event forecasting was also explored. An event was considered to be a ramp event if the change in wind power was 50% or more of total capacity in either 4 or 2 h or less. This was approximated using a typical wind turbine power curve such that any wind speed increase or decrease of more than 3 m s21 within the 6–12 m s21 window (where power production varies greatly) in 4 h or less would be considered a ramp. Model MAE, climatology of ramp events, and causes were examined. All PBL schemes examined predicted fewer ramp events compared to the observations, and model forecasts for ramps in general were poor.

Series Number
Journal Issue
Is Version Of
Versions
Series
Academic or Administrative Unit
Type
article
Comments

This article is from Weather and Forecasting 28 (2013): 212, doi: 10.1175/WAF-D-11-00112.1. Posted with permission.

Rights Statement
Copyright
Tue Jan 01 00:00:00 UTC 2013
Funding
DOI
Supplemental Resources
Collections