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Wind and Structures
  Volume 14, Number 4, July 2011 , pages 337-357

Reconstruction of gusty wind speed time series from autonomous data logger records
Javier Amezcua, Raul Munoz and Oliver Probst

    The collection of wind speed time series by means of digital data loggers occurs in many domains, including civil engineering, environmental sciences and wind turbine technology. Since averaging intervals are often significantly larger than typical system time scales, the information lost has to be recovered in order to reconstruct the true dynamics of the system. In the present work we present a simple algorithm capable of generating a real-time wind speed time series from data logger records containing the average, maximum, and minimum values of the wind speed in a fixed interval, as well as the standard deviation. The signal is generated from a generalized random Fourier series. The spectrum can be matched to any desired theoretical or measured frequency distribution. Extreme values are specified through a postprocessing step based on the concept of constrained simulation. Applications of the algorithm to 10-min wind speed records logged at a test site at 60 m height above the ground show that the recorded 10-min values can be reproduced by the simulated time series to a high degree of accuracy.
Key Words
    wind speed; time series; gusts; kaimal distribution; constrained simulation; autocorrelation function.
Javier Amezcua: Department of Atmospheric and Oceanic Science University of Maryland, College Park 20742-2425, USA
Raul Munoz and Oliver Probst : Physics Department,Instituto Tecnologico y de Estudios Superiores de Monterrey,Eugenio Garza Sada 2501 Sur, Monterrey, N.L., CP 64849, Mexico

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