DecSolNet: A noise resistant missing information recovery framework for daily satellite NO2 columns. (1st February 2021)

• Record Type: Journal Article
• Authors: Zhu, Songyan; Xu, Jian; Yu, Chao; Wang, Yapeng; Efremenko, Dmitry S.; Li, Xiaoying; Sui, Zhengwei
• Is Part Of: Atmospheric environment. Volume 246(2021)
• Publication Date: 2021-02-01
• Subjects: NO2 columns -- Remote sensing -- Data reconstruction -- Time series decomposition -- EMD -- Deep learning; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2020.118143
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: A novel statistical method (hereafter referred to as DecSolNet) for reconstructing satellite NO2 columns is introduced. The method has been developed and evaluated by comparing its performance with four benchmark models in three scenarios. When the amount of satellite data is limited, DecSolNet outperforms the benchmark models and its performance does not degrade with noisy inputs. The implementation of DecSolNet consists of: (1) feature extraction, sequential data decomposition in both temporal and frequency domains; (2) NO2 columns reconstruction by training a deep neural network. In three cross-validations, the averaged R 2 score of DecSolNet reaches 0.9, which is better than that of the most benchmark models. The multi-layer perceptron (MLP) has a higher R 2 score, but it degrades greatly with noisy inputs, while the performance of DecSolNet remains robust with an R 2 of ~ 0.8. The bias of DecSolNet is small with an average of 1.61 μ g / m 3 . In addition, DecSolNet is a reliable learning machine, the averaged loss and standard deviation are 0.42 μ g / m 3 and 0.04 μ g / m 3, respectively. Highlights: Daily missing nitrogen dioxide columns reconstruction is realized by a deep neural network model. Hilbert-Huang transform effectively decomposes atmospheric timeseries into components understandable by neural networks. Complex model structure is resistant to noisy inputs by considering atmospheric physics and photochemical reactions.