Attenuating the Absorption Contribution on $${C_{n^{2}}}$$ Estimates with a Large-Aperture Scintillometer

Springer Science and Business Media LLC - Tập 143 - Trang 261-283 - 2012
P. A. Solignac1, A. Brut1, J. L. Selves1, J. P. Béteille1, J. P. Gastellu-Etchegorry1
1CESBIO, Toulouse Cedex 9, France

Tóm tắt

Large-aperture scintillometers (LAS) are often used to characterize atmospheric turbulence by measuring the structure parameter of the refractive index $${C_{n^{2}}}$$ . However, absorption phenomena can lead to an overestimation of $${C_{n^{2}}}$$ . By applying an accurate numerical filtering technique called the Gabor transform to the signal output of a LAS, we improved our knowledge of the accuracy of the measured $${C_{n^{2}}}$$ by determining and attenuating the contribution of absorption. Two studies are presented on a 12-day dataset using either fixed band pass or adaptive filtering. The first consists of evaluating the best-fit filter for which the resulting $${C_{n^{2}}}$$ is independent of meteorological conditions, especially crosswind, and the second consists in accurately reconstructing the signal to remove absorption, without losing information on $${C_{n^{2}}}$$ . A reference $${C_{n^{2}}}$$ (hereafter ‘reconstructed $${C_{n^{2}}}$$ ’) is created by accurately removing absorption from the scintillation spectrum, and is used to evaluate each filter. By comparing the ‘reconstructed $${C_{n^{2}}}$$ ’ with a raw $${C_{n^{2}}}$$ measured with a scintillometer, in the presence of absorption, we found that the average relative contribution of absorption to the measurement of $${C_{n^{2}}}$$ is approximately 9%. However, the absorption phenomenon is highly variable; occasionally, in the worst cases, we estimate that the absorption phenomenon could represent 81% of the value of $${C_{n^{2}}}$$ . Some explanations for this high variability are proposed with respect to theoretical considerations. Amongst the fixed band-pass filtering used, we conclude on the preferential use of a band-pass filter [0.2–400 Hz] for $${C_{n^{2}}}$$ , as its performance is only slightly affected by the crosswind, and that the mean absorption contribution is reduced to 5.6%, with a maximum value of 60%. Using an adaptive filter on the 12-day dataset really improves the filtering accuracy for both points discussed—the independence of meteorological conditions and the quality of signal reconstruction.

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