Abstract
Aerosol plays a very important role in affecting the earth-atmosphere radiation budget, and particle size distribution is an important aerosol property parameter. Therefore, it is necessary to determine the particle size distribution. However, the particle size distribution determined by the particle extinction efficiency factor according to the Mie scattering theory is an ill-conditioned integral equation, namely, the Fredholm integral equation of the first kind, which is very difficult to solve. To avoid solving such an integral equation, the BP neural network prediction model was established. In the model, the aerosol optical depth obtained by sun photometer CE-318 and kernel functions obtained by Mie scattering theory were used as the inputs of the neural network, particle size distributions collected by the aerodynamic particle sizer APS 3321 were used as the output, and the Levenberg–Marquardt algorithm with the fastest descending speed was adopted to train the model. For verifying the feasibility of the prediction model, some experiments were carried out. The results show that BP neural network has a better prediction effect than that of the RBF neural network and is an effective method to obtain the aerosol particle size distribution of the whole atmosphere column using the data of CE-318 and APS 3321.
Highlights
As an important part of the atmosphere-earth system, aerosols affect the earth-atmosphere radiation budget through direct effects and indirect effects [1]. erefore, changes in aerosol properties can affect many aspects of the atmosphere [2], such as climate, environment, rainfall, and visibility
In order to avoid solving the integral equation, the BP neural network prediction model was employed, in which the aerosol optical depth (AOD) obtained by the CE-318 sun photometer and kernel function obtained by Mie scattering theory were used as the inputs of BP neural network, and the particle size distribution (PSD) collected by the APS-3321 aerodynamic particle sizer was used as the output
Sun photometer is a passive atmospheric remote sensing instrument that can directly detect solar radiation and conduct sky scanning. It has two optical probes, one is employed for sky radiation measurement with a transparent mirror and the other is used for measuring direct solar radiation without a concentrator lens. e measured solar radiation data can be used to calculate the atmospheric transmittance, AOD, total atmospheric vapor column, and total ozone amount. e data of sky radiation scanning data can be used to invert the aerosol PSD, phase function, and other atmospheric optical parameters
Summary
As an important part of the atmosphere-earth system, aerosols affect the earth-atmosphere radiation budget through direct effects (scattering and absorption of solar radiation) and indirect effects (mainly caused by changes in cloud characteristics) [1]. erefore, changes in aerosol properties can affect many aspects of the atmosphere [2], such as climate, environment, rainfall, and visibility. R0 where Qext(r, λ, m) is the kernel function, namely, extinction efficiency factor; m is the complex refractive index of aerosol particles; r is the radius; λ is wavelength; τ(r) is the AOD; and N (r) is the aerosol PSD of whole atmosphere column. Nguyen proposed an improved antifold algorithm to invert particle concentration and size distribution by solving the Fredholm integral equation from extinction coefficient measurements of multiple wavelengths [15]. In order to avoid solving the integral equation, the BP neural network prediction model was employed, in which the AODs obtained by the CE-318 sun photometer and kernel function obtained by Mie scattering theory were used as the inputs of BP neural network, and the PSD collected by the APS-3321 aerodynamic particle sizer was used as the output.
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