Using particle swarm optimization to improve visibility-aerosol optical depth retrieval method

Abstract

Abstract In view of the lack of long-term AOD (Aerosol Optical Depth) data, PSO (Particle Swarm Optimization) algorithm is introduced and joint used with NLSM (the nonlinear least square method) to improve visibility-AOD retrieval method, which is referred to as the PSO-M-Elterman model and significantly increases data available rate by 8% and correlation by about 20% with the true value in the experimental group. The mean absolute error, the proportion of the smaller absolute error and the root mean square error in the PSO-M-Elterman model experimental group are 0.0314 and 91.23%, 0.0509 respectively, which significantly outperforms other groups. The main increase of AOD was found in the eastern region (South China, East China, Central China) and Taklimakan with the trend coefficients of 2.67, 2.46, 2.13, and 1.45 (×10−3 yr−1) in recent 55 years, which may not be interpreted by the influence of relative humidity. Long-term change of AOD in east China is mainly caused by human activity, and the AOD is higher in cities with a larger population and more human activity. The PSO-M-Elterman model can maximize the advantage of visibility sequence length to obtain long-term AOD inversion results.