期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 10, 期 12, 页码 5442-5457出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2017.2740168
关键词
Atmospheric composition measurements; machine learning; ozone profiles
类别
资金
- Bayerisches Staatsministerium fur Wirtschaft und Medien, Energie und Technologie [0703/89373/15/2013]
- DLR programmatic [S5P KTR 2 472 046]
Identifying ozone profile shapes from nadir-viewing satellite sensors is a critical yet challenging task for accurate reconstruction of vertical distributions of ozone relevant to climate change and air quality. Motivated by the need to develop a methodology to fast, reliably, and efficiently exploit ozone distributions and inspired by the success of machine learning, this paper introduces a novel algorithm for estimating ozone profile shapes from satellite ultraviolet absorption spectra. The Full-Physics Inverse Learning Machine (FP-ILM) algorithm successfully characterizes ozone profile shapes using machine learning approaches. Its implementation mainly consists of a clustering process based on a semi-supervised agglomerative algorithm, a classification process based on full-physics radiative transfer simulations and a neural network (NN), and a profile scaling process based on a NN ensemble. The classification model has been trained with synthetic data generated by a forward model in conjunction with smart sampling, while the scaling model corresponding to each cluster requires total ozone information. The main innovation of FP-ILM is that, unlike conventional inversion methods, the ozone profile retrieval is formulated as a classification problem, leading to a noteworthy speed-up and accuracy when dealing with applications of satellite data. An outstanding retrieval performance with errors of less than 10% over 100-1 hPa has been obtained for synthetic measurements. Furthermore, the ozone profiles retrieved from the Global Ozone Monitoring Experiment-2 data using FP-ILM and the optimal estimation method reach an encouraging agreement (the differences are less than 6 Dobson Units or within 5%-20%).
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