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  • 16th International Symposium for the Advancement of Boundary-Layer Remote Sensing (ISARS 2012) x
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Qing Yang, Larry K. Berg, Mikhail Pekour, Jerome D. Fast, Rob K. Newsom, Mark Stoelinga, and Catherine Finley

features in the UW scheme are likely to improve boundary layer wind predictions. The goal of this research is to characterize the ramp occurrence over the CBWES site that is within an operating wind farm, to evaluate the WRF model's capability in ramp prediction, and to test the sensitivity of the model's performance to the choice of PBL schemes. Results of this study are also intended to provide recommendations to the wind-energy community regarding the choice of PBL schemes in WRF in areas of complex

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S.-E. Gryning, E. Batchvarova, and R. Floors

. Comparison between model and measurements: slope coefficient a of a linear fit through the origin ( Y = aX , where Y is the modeled wind speed and X is the measured wind speed); bias , where the bar denotes average; normalized bias , and RMSE { , where N is the number of samples}. The root-mean-square error (RMSE) is used to illustrate the comparison between the individual measurements and the model prediction. There is a clear improvement in the performance of the model when it is nudged

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David Schlipf, Po Wen Cheng, and Jakob Mann

work also considers wind evolution (see Bossanyi 2012 ; Simley et al. 2012 ; Laks et al. 2013 ). Initial field testing of lidar-assisted feedforward collective pitch control shows that improvement of the control performance can be confirmed in reality and proves that the performance of lidar-assisted control is dependent on the correlation between the lidar system and the turbine (see Schlipf et al. 2012a ; Scholbrock et al. 2013 ). In this work, a new analytic model of correlation between the

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A. B. White, M. L. Anderson, M. D. Dettinger, F. M. Ralph, A. Hinojosa, D. R. Cayan, R. K. Hartman, D. W. Reynolds, L. E. Johnson, T. L. Schneider, R. Cifelli, Z. Toth, S. I. Gutman, C. W. King, F. Gehrke, P. E. Johnston, C. Walls, D. Mann, D. J. Gottas, and T. Coleman

J.-W. , Neiman P. J. , Schultz P. J. , Yuan H. , and White A. B. , 2009 : Evaluation and comparison of microphysical algorithms in WRF-ARW model simulations of atmospheric river events affecting the California coast . J. Hydrometeor. , 10 , 847 – 870 . Jankov, I. , and Coauthors , 2011 : An evaluation of five WRF-ARW microphysics schemes using synthetic GOES imagery for an atmospheric river event affecting the California coast . J. Hydrometeor. , 12 , 618 – 633 . Jian, G

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C. R. Wood, R. D. Kouznetsov, R. Gierens, A. Nordbo, L. Järvi, M. A. Kallistratova, and J. Kukkonen

-small segments lead to the decrease of the structure parameter accuracy at low values, when the spectrum is strongly affected by measurement uncertainties. This effect was most pronounced for low values of the temperature structure parameters. If segments are too long, then the signal from beyond the inertial subrange appears in the estimated spectrum. This results in a degradation of fitting performance of the model spectrum [Eq. (6) ] and a corresponding increase in chi square. The choice of a segment

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Mikael Sjöholm, Nikolas Angelou, Per Hansen, Kasper Hjorth Hansen, Torben Mikkelsen, Steinar Haga, Jon Arne Silgjerd, and Neil Starsmore

WindScanners were scanning the entire horizontal plane and the mean speed in each 1-m-long grid cell was evaluated. In Fig. 7 , the 41-s-average horizontal wind speeds obtained from the ultrasonic anemometer are plotted in black and the 10-min-average horizontal speeds obtained from the WindScanner system are plotted in red. The gray area is based on the maximum and minimum of the WindScanner-measured 12-s-average horizontal speeds in each grid cell obtained during the 10-min-long measurement period. The

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