title: Inversion of the haze aerosol sky columnar AVSD in central China by combining multiple ground observation equipment.
abstract: Wuhan is the biggest city in China that has been facing an increasingly serious problem of air pollution in the recent years. In order to understand the mechanism of haze formation and diffusion, it is very important to obtain multiple atmospheric parameters. Columnar aerosol volume size distribution (AVSD) is an important atmospheric parameter in this regard, and utilizing CIMEL sun-photometer data to obtain this parameter has become the most popular method. However, currently, the widely used retrieval algorithms cannot be accessed using an open source code, and thus the retrieval of columnar AVSD is still a challenging task.. In this article, we introduce a new method that combines partial least squares (PLS) and genetic algorithm (GA) for the retrieval of columnar AVSD. By using this new method, we could obtain credible results even during hazy periods, despite the fact that our sun-photometer did not participate in the AERONET program and we did not use an official data processing method. First, it was assumed that columnar AVSD obeys the double logarithmic normal distribution function. Second, the relationship between the columnar AVSD and the AVSD on earth's surface was established using the partial least squares (PLS) method. Finally, the initial distribution parameters were adjusted through GA to obtain an optimal solution. This new method can improve the accuracy and reduce the computational difficulties faced in the retrieval of columnar AVSD in the absence of AREONET-based algorithm.
authors.forename: Yingying
authors.initials: Y
authors.lastname: Ma
authors.forename: Wei
authors.initials: W
authors.lastname: Gong
authors.forename: Lunche
authors.initials: L
authors.lastname: Wang
authors.forename: Ming
authors.initials: M
authors.lastname: Zhang
authors.forename: Zhongyong
authors.initials: Z
authors.lastname: Chen
authors.forename: Jun
authors.initials: J
authors.lastname: Li
authors.forename: Jian
authors.initials: J
authors.lastname: Yang