Novel Inversion Algorithm for the Atmospheric Aerosol Extinction Coefficient Based on an Improved Genetic Algorithm

Abstract

As an important atmospheric component, aerosols play a very important role in the radiation budget balance of the earth–atmosphere system. To study the optical characteristics of aerosols, it is necessary to use an inversion algorithm to process the lidar return signal to obtain both the aerosol extinction coefficient and the backscattering coefficient. However, the lidar return power equation is ill-conditioned and contains two unknown parameters, meaning that traditional inversion algorithms must be solved by adopting certain assumptions (e.g., a uniform atmosphere and the lidar ratio), which to a certain extent can seriously affect the inversion accuracy. Here, to improve the accuracy of the aerosol extinction coefficient inversion, an inversion method based on an improved genetic algorithm is proposed. Using the U.S. Standard Atmosphere model and the return power equation, the aerosol extinction coefficient and the backscattering coefficient are independent variables that randomly provide initial values to simulate the theoretical lidar power. Then, the genetic algorithm is used to approximate the theoretical lidar power to the measured lidar return power with height; when the two are infinitely close, the values of the corresponding two independent variables (i.e., the extinction and backscattering coefficients) are inverted. Experiments performed to compare the different effects between a simple genetic algorithm and the improved genetic algorithm showed the proposed method capable of inverting the aerosol extinction coefficient without reliance on traditional inversion methods, representing a novel approach to the inversion of the aerosol extinction coefficient and the backscattering coefficient.

A novel lidar signal noise reduction algorithm based on improved deep belief network

“…In practical applications, the source of noise in a lidar return signal is very complex. It is assumed that the real noise is the sum of many independent random variables, which conform to the Gaussian distribution diagram [19]. To verify that the DADBN method is a superior option, it is compared to four other methods, namely, the wavelet packet algorithm, complete ensemble empirical modal decomposition (CEEMDAN), wavelet transform and empirical mode decomposition (WT-EMD), and wavelet transform-variational mode decomposition (WT-VMD).…”

“…The convolutional layer is used to replace the fully connected layer of the autoencoder. After constructing a convolutional neural network to learn the depth features of the lidar signal, the signal details are reconstructed through the decoding part to obtain the denoised signal [19].…”