Attenuating the Absorption Contribution on C_n2{C_{n^{2}}} Estimates with a Large-Aperture Scintillometer

摘要

Large-aperture scintillometers (LAS) are often used to characterize atmospheric turbulence by measuring the structure parameter of the refractive index C_n2{C_{n^{2}}} . However, absorption phenomena can lead to an overestimation of C_n2{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_n2{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_n2{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_n2{C_{n^{2}}} . A reference C_n2{C_{n^{2}}} (hereafter ‘reconstructed C_n2{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_n2{C_{n^{2}}} ’ with a raw C_n2{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_n2{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_n2{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_n2{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.