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9 October 2019 On estimation of cloud characteristics from spectral measurements of scattered solar radiation using a neural network
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Abstract
Light scattering by cloudiness and aerosol have a significant impact on the possibility of quantitative estimation of the content of NO2, H2CO and other trace gases in the lower troposphere using the MAX-DOAS and ZDOAS techniques. Since there is a large volume of optical observations of trace gases by these techniques that are not accompanied by measurements of their characteristics, solving the problem of determining the properties of clouds and aerosol from the spectral measurements themselves could increase the accuracy of measuring trace gases. The paper considers the tasks of determining the characteristics of clouds (the bottom height, the optical depth, etc.) and aerosol (the optical depth, the vertical distribution parameters, etc.) from quantitates obtained from ZDOAS measurements (the O4 slant column, the color index, the absolute intensity, etc.). We performed numerical experiments for retrieving clouds and aerosol characteristics basing on radiative transfer simulations in cloudy atmosphere. A neural network is used as a method for solving emerging nonlinear estimation problems, the accuracy of the evaluation is determined on the training set, and a control set is used to characterize the agreement of the evaluation results (i.e., how much confidence can be given to the parameter estimation and its error).
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Stanislav V. Nikitin, Alexey I. Chulichkov, Alexander N. Borovski, and Oleg V. Postylyakov "On estimation of cloud characteristics from spectral measurements of scattered solar radiation using a neural network", Proc. SPIE 11152, Remote Sensing of Clouds and the Atmosphere XXIV, 111521H (9 October 2019); https://doi.org/10.1117/12.2535490
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