The observation of polar clouds is of great significance to studying climate change in Antarctica. The modified Klett–Fernald method and Mie scattering light detection and ranging (Lidar) were used to study the characteristics of low-altitude clouds from February 15 to March 2, 2017 at the Great Wall Station. The temporal and spatial variations in the cloud extinction coefficient were obtained, and the optical and physical characteristics of the low-altitude clouds were statistically analyzed. The analysis shows that the near-ground atmosphere is very clean and the atmosphere at low heights is covered by low-altitude clouds. The meteorological and Lidar data show that the polar cyclone may be the main driving force of the cloud-height changes at this station.
Differential column image motion lidar (DCIM lidar) can obtain the Fried’s transverse coherence length (r0) of different altitudes with a high spatial and temporal resolution. According to the integral equation of atmospheric coherence length (r0) of spherical wave, the refractive structure constant(C2n) profile can be retrieved from r0 profile. Aiming at improving the retrieval accuracy of atmospheric turbulence profile, noise reduction on r0 profile is implemented before inversion. Two methods of wavelet threshold and complementary ensemble empirical mode decomposition (CEEMD) are used to denoise r0 profile. The effects of denoised methods on r0 profile and C2n profile are investigated. The numeric simulations and experiments are both carried out to validate the two denoised methods. The results show that both the two methods can improve the signal-to-noise ratio (SNR) of atmospheric coherent length profile and reduce the recovered error of the atmospheric turbulence profile, and wavelet threshold method is superior to CEEMD method under different noise conditions.
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