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B. Van Dam, D. Helmig, W. Neff, and L. Kramer

BLD. A linear regression analysis was conducted for each dataset, with the data weighted by the fraction of the total number of observations available in each bin [indicated by the gray bars in (a), using the same y -axis scale but with the units being decimal fraction (i.e., ranging from 0 to 0.3, or from 0% to 30% of total observations; the same weighting was used in (b)]. The linear regression R 2 value is 0.83 in (a) and is 0.29 for (b). b. BLD estimation Equations (1) and (3) were used

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Andreas Rettenmeier, David Schlipf, Ines Würth, and Po Wen Cheng

( υ 0 L ). A simple regression analysis was carried out. Because the model is linear with a single independent variable, the coefficient of determination ( R 2 ) is equal to the square of the Pearson correlation coefficient r xy . In statistics r xy is a measure of the linear correlation and indicates the dependence between two variables. In both figures a certain offset and slope were detected that are caused by the estimation of the rotor effective wind speed. For a more precise estimation

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Jacob Berg, Jakob Mann, and Edward G. Patton

measurements of turbulent fluxes in the streamwise direction mentioned in the introduction, Mann et al. (2010) also found that for heights above approximately 50 m, there is a constant systematic error when measuring 〈 u ′ w ′〉 of approximately 20%. In the data analysis performed in this manuscript, the transverse component 〈 υ ′ w ′〉 is of the same order of magnitude as the streamwise component 〈 u ′ w ′〉. We used the same WindCube as that in Mann et al. (2010) for estimating both the streamwise and

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