A feasibility study to use machine learning as an inversion algorithm for aerosol profile and property retrieval from multi-axis differentialabsorption spectroscopy measurements

GSTDTAP > 资源环境科学

DOI	10.5194/amt-13-5537-2020
	A feasibility study to use machine learning as an inversion algorithm for aerosol profile and property retrieval from multi-axis differentialabsorption spectroscopy measurements
	admin
	2020-11-12
出版年	2020
国家	欧洲
领域	气候变化 ; 资源环境
英文摘要	This paper is about a feasibility study of applying a machine learning technique to derive aerosol properties from a single MAX-DOAS sky scan, which detects sky-scattered UV–visible photons at multiple elevation angles. Evaluation of retrieved aerosol properties shows good performance of the ML algorithm, suggesting several advantages of a ML-based inversion algorithm such as fast data inversion, simple implementation and the ability to extract information not available using other algorithms.
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来源平台	European Geosciences Union
引用统计
文献类型	科技报告
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/303648
专题	资源环境科学气候变化
推荐引用方式 GB/T 7714	admin. A feasibility study to use machine learning as an inversion algorithm for aerosol profile and property retrieval from multi-axis differentialabsorption spectroscopy measurements,2020.