ATMOSPHERIC LIDAR NOISE REDUCTION BASED ON ENSEMBLE EMPIRICAL MODE DECOMPOSITION

Abstract

Abstract. As an active remote sensing instrument, lidar provides a high spatial resolution vertical profile of aerosol optical properties. But the effective range and data reliability are often limited by various noises. Performing a proper denoising method will improve the quality of the signals obtained. The denoising method based on ensemble empirical mode decomposition (EEMD) is introduced, but the denoised results are difficult to evaluated. A dual field-of-view lidar for observing atmospheric aerosols is described. The backscattering signals obtained from two channels have different signal-to-noise ratios (SNR). To overcome the drawback of the simulation experiment, the performance of noise reduction can be investigated by comparing the high SNR signal and the denoised low SNR signal. With this approach, some parameters of the denoising method based on EEMD can be determined effectively. The experimental results show that the EEMD-based method with proper parameters can effectively increase the atmospheric lidar observing ability.