SIMULATING ATMOSPHERIC-TURBULENCE BY SYNTHETIC REALIZATION OF TIME-SERIES IN RELATION TO POWER SPECTRA

A novel and-direct procedure is presented for the realization of atmospheric turbulence by an appropriately estimated autoregressive model. Straightforward identification of the model parameters is based upon the Yule-Walker equation, which expresses the autocorrelation in terms of the model parameters. These are adjusted as part of a hill climbing process until a given target autocorrelation is achieved. The target function is obtained directly through Fourier transformation of the spectral density function representing the desired atmospheric turbulence characteristics. For this study the Kaimal spectrum for turbulence in the lower boundary layer has been adopted. Validation of the procedure is presented in which a time series generated from the model is Fourier transformed for comparison with the target spectral density. For a fourth order autoregressive model, good agreement was found for the frequency range of interest (0.001-1.0 Hz). The approach outlined is straightforward to implement, requiring minimal computational effort once the model parameters have been identified. It is well suited to application on a PC and should find wide applicability in such fields as meteorology, wind energy and structural engineering.