An RBF neural network approach for retrieving atmospheric extinction coefficients based on lidar measurements

Abstract

Lidar is an effective remote sensing method for obtaining the optical properties of aerosols, such as the aerosol extinction coefficient (AEC), the aerosol optical depth (AOD), and the related atmospheric visibility. However, improving the accuracy and efficiency of lidar data retrieval remains challenging due to the uncertainties associated in determining the AEC boundary value (AEC-BV) and the aerosol extinction-to-backscatter ratio (AEBR), as well as the complex and time-consuming calculations required. In this paper, we propose a novel method, a feedback radial basis function (RBF-FB), for retrieving high-precision AEC profiles based on a radial basis function neural network. First, using the secant method, we determine accurate values for AEC-BV and AEBR, and generate the AEC profiles by the Fernald method. We then choose a set of lidar signals and their corresponding AEC profiles as learning samples for network training and establish an RBF network model for AEC retrieval. Next, we correct the network output by introducing a feedback mechanism that uses the AOD measured by a sun photometer as the error criterion. Tests on measured signals confirm that the outputs of the proposed RBF-FB model are consistent with the Fernald method and have the advantages of speed and robustness.