LAUSR

Abstract Mie lidar is a powerful tool for detecting the optical properties of atmospheric aerosols. However, there are two unknown parameters in the Mie lidar equation: the extinction coefficient and the backscattering coefficient. In the common methods for solving the equation, it is necessary to make assumptions about the relationship between the two unknown parameters. These assumptions will reduce the detection precision of extinction coefficient. In view of this, the back propagation (BP) neural network is used to retrieve extinction coefficient from the Mie scattering signal of lidar. Firstly, the structure and main parameters of the BP network are designed according to the practical application. In order to improve the convergence speed and prevent falling into local minima, the initial weights and thresholds of BP network are optimized by genetic algorithm (GA). Then the GA-BP network is trained with Mie scattering signal and the extinction coefficient retrieved by Raman method. Thus the mathematical relationship between Mie scattering signal and the extinction coefficient is stored in the BP network. The trained GA-BP network is then used to retrieve the extinction coefficient from Mie scattering signal in different conditions and the applicability of the GA-BP network is researched. The research will promote the development of Mie lidar retrieving algorithm.