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.
Highlights
1.1 General InstructionsAerosol can directly affect climate change by scattering and absorption of solar and other radiation, and indirectly affect the radiation by affecting cloud formation
The lidar data inversion is sensitive to the lidar data at a far distance, which are under low signal-to-noise ratio conditions
Through a sifting process described by Huang et al, the signal can be decomposed into a series of intrinsic mode functions (IMF) and the residual through the sifting process
Summary
Aerosol can directly affect climate change by scattering and absorption of solar and other radiation, and indirectly affect the radiation by affecting cloud formation. Where Vmeasured (r) = signal measured V(r) = signal from aerosol backscattering Nb(r) = noise due to background light Ne(r) = noise due to dark current and read out electronics. The signal-to-noise ratio (SNR) falls as the range increases, and the solution for the lidar equation becomes unstable and even fails because of the negative value produced by noise. There are several signal analysis methods widely adopted for the noise reduction in the lidar signal. Most lidar systems employ the multiple pulses averaging to enhance SNR. This method can be considered as a low pass filtering process at the cost of temporal resolution, high frequency backscattering signal is smoothed. Wavelet analysis is developed rapidly as an effective tool for noise reduction[2]. The selection of the best basis function is a hard work
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
