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Laura Bianco, Daniel Gottas, and James M. Wilczak

1. Introduction The implementation of real-time data quality control is of fundamental importance for observations that are assimilated into operational numerical weather prediction models. One of the most vexing quality-control problems affecting radar wind profilers has been signal contamination from nocturnally migrating birds ( Wilczak et al. 1995 ). Although techniques have been developed that helped reduce the level of contamination ( Wilczak et al. 1995 ; Merritt 1995 ), these were

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Stuart Bradley

the essence of whether remote sensing gives “bankable” data. What quality of wind measurements can be expected from remote sensing in a typical installation? This is relevant since intercomparisons are often under test conditions unlike those typically encountered at normal sites. Some previous intercomparison experiments have touched on the spatial and temporal separation issues treated in detail below. Mastrantonio and Fiocco (1982) describe transmission on three acoustic beams simultaneously

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Elías Lau, Scott McLaughlin, Frank Pratte, Bob Weber, David Merritt, Maikel Wise, Gary Zimmerman, Matthew James, and Megan Sloan

; Weber et al. 1993 ). Typically, winds are reported in 6-min and 1-h intervals. In the 6-min case, the data collected in the last 60 min are retrieved and ASPEN runs quality control checks on the data, but only the last 30 min are used to calculate an average profile. For the wind profiles that are reported every hour, the typical quality control time is 4 h and the averaging time is 1 h. The long average time helps to satisfy the horizontal homogeneity assumption ( Cheong et al. 2008 ). BIRCH is

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Leslie M. Hartten and Paul E. Johnston

s and the spacing between vertical dwells in the same mode was about 5.3 min. In autumn 2004, the dwell duration was about 40 s for the low mode and 30 s for the high mode and the spacing between vertical dwells of the same mode was about 6 min. Figure 2 illustrates the effects of our quality control on one day’s low-mode data from each cruise; SNR was converted to relative reflectivity before plotting. The original dataset contains some SNRs and spectral widths that are unrealistically large

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David Schlipf, Po Wen Cheng, and Jakob Mann

evaluated with data from the field testing in section 5 . The model is used in section 6 to optimize a lidar system, and conclusions and future work are discussed in section 7 . 2. Requirements for control Reducing fatigue and extreme loads of the structure is an important design goal for the control of large wind turbines. Transient events such as gusts represent an unknown disturbance to the control system. Conventional feedback controllers can only provide delayed compensation for such

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

resulting value can be estimated from the fitting error of , and the goodness-of-the-fit of the model spectrum can be estimated with the Pearson chi-square test ( Press et al. 2007 ). These values also allow for the quality control of the resulting structure parameters. Fig . 2. An example power spectral density from sonic-anemometer data for virtual temperature on 14 May 2011 in downtown Helsinki (at Hotel Torni). Shown with fitted spectra: full model, the model without the aliasing term , the model

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