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- Author or Editor: Oscar K. Hartogensis x
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Abstract
In this study the crosswind (U ⊥) is determined from the time-lag correlation function [r 12(τ)] measured by a dual large-aperture scintillometer; U ⊥ is defined as the wind component perpendicular to a path—in this case, the scintillometer path. A scintillometer obtains a path-averaged U ⊥, which for some applications is an advantage compared to other wind measurement devices. Four methods were used to obtain U ⊥: the peak method, the Briggs method, the zero-slope method, and the lookup table method. This last method is a new method introduced in this paper, which obtains U ⊥ by comparing r 12(τ) of a measurement to r 12(τ) of a theoretical model. The U ⊥ values obtained from the scintillometer were validated with sonic anemometer measurements. The best results were obtained by the zero-slope method for U ⊥ < 2 m s−1 and by the lookup table method for U ⊥ > 2 m s−1. The Briggs method also showed promising results, but it is not always able to obtain U ⊥. The results showed that a high parallel wind component (>2.5 m s−1) on the scintillometer path can cause an overestimation of U ⊥ mainly for low U ⊥ values (<2 m s−1).
Abstract
In this study the crosswind (U ⊥) is determined from the time-lag correlation function [r 12(τ)] measured by a dual large-aperture scintillometer; U ⊥ is defined as the wind component perpendicular to a path—in this case, the scintillometer path. A scintillometer obtains a path-averaged U ⊥, which for some applications is an advantage compared to other wind measurement devices. Four methods were used to obtain U ⊥: the peak method, the Briggs method, the zero-slope method, and the lookup table method. This last method is a new method introduced in this paper, which obtains U ⊥ by comparing r 12(τ) of a measurement to r 12(τ) of a theoretical model. The U ⊥ values obtained from the scintillometer were validated with sonic anemometer measurements. The best results were obtained by the zero-slope method for U ⊥ < 2 m s−1 and by the lookup table method for U ⊥ > 2 m s−1. The Briggs method also showed promising results, but it is not always able to obtain U ⊥. The results showed that a high parallel wind component (>2.5 m s−1) on the scintillometer path can cause an overestimation of U ⊥ mainly for low U ⊥ values (<2 m s−1).
Abstract
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Abstract
In this study, spectral techniques to obtain crosswinds from a single large-aperture scintillometer (SLAS) time series are investigated. The crosswind is defined as the wind component perpendicular to a path. A scintillometer obtains a path-averaged estimate of the crosswind. For certain applications this can be advantageous (e.g., monitoring crosswinds along airport runways). The essence of the spectral techniques lies in the fact that the scintillation power spectrum shifts linearly along the frequency domain as a function of the crosswind. Three different algorithms are used, which are called herein the corner frequency (CF), maximum frequency (MF), and cumulative spectrum (CS) techniques. The algorithms track the frequency shift of a characteristic point in different representations of the scintillation power spectrum. The spectrally derived crosswinds compare well with sonic anemometer estimates. The CS algorithm obtained the best results for the crosswind when compared with the sonic anemometer. However, the MF algorithm was most robust in obtaining the crosswind. Over short time intervals (<1 min) the crosswind can be obtained with the CS algorithm using wavelet instead of fast Fourier transformation to calculate the power scintillation spectra.
Abstract
In this study, spectral techniques to obtain crosswinds from a single large-aperture scintillometer (SLAS) time series are investigated. The crosswind is defined as the wind component perpendicular to a path. A scintillometer obtains a path-averaged estimate of the crosswind. For certain applications this can be advantageous (e.g., monitoring crosswinds along airport runways). The essence of the spectral techniques lies in the fact that the scintillation power spectrum shifts linearly along the frequency domain as a function of the crosswind. Three different algorithms are used, which are called herein the corner frequency (CF), maximum frequency (MF), and cumulative spectrum (CS) techniques. The algorithms track the frequency shift of a characteristic point in different representations of the scintillation power spectrum. The spectrally derived crosswinds compare well with sonic anemometer estimates. The CS algorithm obtained the best results for the crosswind when compared with the sonic anemometer. However, the MF algorithm was most robust in obtaining the crosswind. Over short time intervals (<1 min) the crosswind can be obtained with the CS algorithm using wavelet instead of fast Fourier transformation to calculate the power scintillation spectra.
