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

Abstract

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.