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Интеллектуальная Система Тематического Исследования НАукометрических данных |
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On estimation of cloudiness characteristics and parameters of DOAS retrieval from spectral measurements using a neural networkстатья
- Авторы: Postylyakov O.V., Borovski A.N., Chulichkov A.I., Nikitin S.V.
- Журнал: IOP Conference Series: Earth and Environmental Science
- Том: 488
- Год издания: 2020
- Издательство: IOP Publishing
- Местоположение издательства: [Bristol, UK], England
- DOI: 10.1088/1755-1315/489/1/012031
- Аннотация: Light scattering by clouds significantly affects the values associated with the contentof NO2, H2CO and other small gases in the lower troposphere, which are obtained by thedifferential optical absorption spectroscopy (DOAS) technique. Since there are a largedatabases of optical observations of trace gases by DOAS technique that are not accompaniedby other measurements of clouds, the development of approaches to the refinement ofscattering characteristics and coefficients linking the DOAS slant column depth with the gasvertical content directly from spectral measurements remains an important task. The paperconsiders the tasks of determining the coefficient F used for transformation of the DOAS slantcolumn depth of a gas to its vertical column from quantitates obtained from ZDOASmeasurements (the O4 slant column, the color index, the absolute intensity, etc.). It was shownin numerical experiments that an algorithm based on a neural network can estimate thecoefficient F in cloudy conditions. It looks like the better approach that two step estimation ofthis parameter using a neural network for estimation of cloud characteristics in the first stepwith the following radiative transfer simulation at the second step.
- Добавил в систему: Чуличков Алексей Иванович