Uncertainty Analysis of Long Term Correction Methods for Annual Average Winds

Abstract: For the construction of a wind farm, one needs to assess the wind resources of the considered site location. Using reference time series from numerical weather prediction models, global assimilation databases or observations close to the area considered, the on-site measured wind speeds and wind directions are corrected in order to represent the actual long-term wind conditions. This long-term correction (LTC) is in the typical case performed by making use of the linear regression within the Measure-Correlate-Predict (MCP) method. This method and two other methods, Sector-Bin (SB) and Synthetic Time Series (ST), respectively, are used for the determination of the uncertainties that are associated with LTC.The test area that has been chosen in this work, is located in the region of the North Sea, using 22 quality controlled meteorological (met) station observations from offshore or nearby shore locations in Denmark, Norway and Sweden. The time series that has been used cover the eight year period from 2002 to 2009 and the year with the largest variability in the wind speeds, 2007, is used as the short-term measurement period. The long-term reference datasets that have been used are the Weather Research and Forecast model, based on both ECMWF Interim Re-Analysis (ERA-Interim) and National Centers for Environmental Prediction Final Analysis (NCEP/FNL), respectively and additional reference datasets of Modern Era Re-Analysis (MERRA) and QuikSCAT satellite observations. The long-term period for all of the reference datasets despite QuikSCAT, correspond to the one of stations observations. The QuikSCAT period of observations used cover the period from November 1st, 1999 until October 31st, 2009.The analysis is divided into three parts. Initially, the uncertainty connected to the corresponding reference dataset, when used in LTC method, is investigated. Thereafter the uncertainty due to the concurrent length of the on-site measurements and reference dataset is analyzed. Finally, the uncertainty is approached using a re-sampling method of the Non-Parametric Bootstrap. The uncertainty of the LTC method SB, for a fixed concurrent length of the datasets is assessed by this methodology, in an effort to create a generic model for the estimation of uncertainty in the predicted values for SB.The results show that LTC with WRF model datasets based on NCEP/FNL and ERA-Interim, respectively, is slightly different, but does not deviate considerably in comparison when comparing with met station observations. The results also suggest the use of MERRA reference dataset in connection with long-term correction methods. However, the datasets of QuikSCAT does not provide much information regarding the overall quality of long-term correction, and a different approach than using station coordinates for the withdrawal of QuikSCAT time series is preferred. Additionally, the LTC model of Sector-Bin is found to be robust against variation in the correlation coefficient between the concurrent datasets. For the uncertainty dependence of concurrent time, the results show that an on-site measurement period of one consistent year or more, gives the lowest uncertainties compared to measurements of shorter time. An additional observation is that the standard deviation of long-term corrected means decreases with concurrent time. Despite the efforts of using the re-sampling method of Non-Parametric Bootstrap the estimation of the uncertainties is not fully determined. However, it does give promising results that are suggested for investigation in further work.