Aerosol Microphysical Particle Parameter Inversion and Error Analysis Based on Remote Sensing Data

Abstract

The use of Raman and high-spectral lidars enables measurements of a stratospheric aerosol extinction profile independent of backscatter, and a multi-wavelength (MW) lidar can obtain additional information that can aid in retrieving the microphysical characteristics of the sampled aerosol. The inversion method for retrieving aerosol particle size distributions and microphysical particle parameters from MW lidar data was studied. An inversion algorithm for retrieving aerosol particle size distributions based on the regularization method was established. Based on the inversion of regularization, the inversion method was optimized by choosing the base function closest to the aerosol distribution. The logarithmic normal distribution function was selected over the triangle function as the base function for the inversion. The averaging procedure was carried out for three main types of aerosol. The 1% averaging result near the minimum of the discrepancy gave the best estimate of the particle parameters. The accuracy and stabilization of the optimized algorithm for microphysical parameters were tested by scores of aerosol size distributions. The systematic effects and random errors impacting the inversion were also considered, and the algorithm was tested by the data, showing 10% systematic error and 15% random error. At the same time, the reliability of the proposed algorithm was also verified by using the aerosol particle size distribution data of the aircraft. The inversion results showed that the algorithm was reliable in retrieving the aerosol particle size distributions at vertical heights using lidar data.In the last 20 years, research has been performed on solving the underlying inverse integral equation system for obtaining the microphysical parameters of aerosol particles from their optical properties. The regularization algorithm [6], the principal component analysis (PCA) technique [13], and the linear estimation algorithm [14] have been used for determining the aerosol bulk properties. The regularization algorithm is used most commonly for inverting multi-wavelength measurements [6], allowing the retrieval of particle size, concentration, and to some extent the main features of the particle size distribution.Using the regularized inversion algorithm, the particle size distribution and microphysical parameters of aerosols are obtained without assuming the initial complex refractive index and aerosol distribution. The inversion results are unstable, and there will be good results under certain spectral types; however, in some cases, the inversion error is very large. In Veselovskii's modified regularized inversion algorithm [15,16], the effective radius, number, surface area, and volume concentration for three distributions were retrieved with an average accuracy of 55%, 70%, 40%, and 50%, respectively. D. analyzed the effects of systematic and random errors on particle microphysical properties from multi-wavelength lidar measurements using an inversion with regularization. Three ASDs were used for this analys...

Research status and progress of lidar for atmosphere in China

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“…Xi'an University of Technology, Anhui Institute of Optics and Fine Mechanics, The Institute of Atmospheric Physics, Chinese Academy of Sciences, Lanzhou University, Northern Minzu University have developed three wavelengths and Raman lidar experimental system 4‐6 . At present, the deviation of microphysical parameters retrieved from multiwavelength lidar is basically 30% – 50% 7 . In addition to three wavelength lidar, dual wavelength (excitation wavelength: 355 and 532 nm) lidar is also widely used in atmospheric detection.…”

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