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Estimation of Lower-Stratosphere-to-Troposphere Ozone Profile Using Long Short-Term Memory (LSTM)

State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China
University of Chinese Academy of Sciences, Beijing 100049, China
Guangxi Eco-Environmental Monitoring Center, Nanning 530028, China
School of Computing, Ulster University, Belfast BT37 0QB, UK
School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China
Author to whom correspondence should be addressed.
Academic Editor: Chris G. Tzanis
Remote Sens. 2021, 13(7), 1374;
Received: 5 February 2021 / Revised: 29 March 2021 / Accepted: 30 March 2021 / Published: 2 April 2021
(This article belongs to the Special Issue Artificial Intelligence in Remote Sensing of Atmospheric Environment)
Climate change and air pollution are emerging topics due to their possible enormous implications for health and social perspectives. In recent years, tropospheric ozone has been recognized as an important greenhouse gas and pollutant that is detrimental to human health, agriculture, and natural ecosystems, and has shown a trend of increasing interest. Machine-learning-based approaches have been widely applied to the estimation of tropospheric ozone concentrations, but few studies have included tropospheric ozone profiles. This study aimed to predict the Northern Hemisphere distribution of Lower-Stratosphere-to-Troposphere (LST) ozone at a pressure of 100 hPa to the near surface by employing a deep learning Long Short-Term Memory (LSTM) model. We referred to a history of all the observed parameters (meteorological data of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), satellite data, and the ozone profiles of the World Ozone and Ultraviolet Data Center (WOUDC)) between 2014 and 2018 for training the predictive models. Model–measurement comparisons for the monitoring sites of WOUDC for the period 2019–2020 show that the mean correlation coefficients (R2) in the Northern Hemisphere at high latitude (NH), Northern Hemisphere at middle latitude (NM), and Northern Hemisphere at low latitude (NL) are 0.928, 0.885, and 0.590, respectively, indicating reasonable performance for the LSTM forecasting model. To improve the performance of the model, we applied the LSTM migration models to the Civil Aircraft for the Regular Investigation of the Atmosphere Based on an Instrument Container (CARIBIC) flights in the Northern Hemisphere from 2018 to 2019 and three urban agglomerations (the Sichuan Basin (SCB), North China Plain (NCP), and Yangtze River Delta region (YRD)) between 2018 and 2019. The results show that our models performed well on the CARIBIC data set, with a high R2 equal to 0.754. The daily and monthly surface ozone concentrations for 2018–2019 in the three urban agglomerations were estimated from meteorological and ancillary variables. Our results suggest that the LSTM models can accurately estimate the monthly surface ozone concentrations in the three clusters, with relatively high coefficients of 0.815–0.889, root mean square errors (RMSEs) of 7.769–8.729 ppb, and mean absolute errors (MAEs) of 6.111–6.930 ppb. The daily scale performance was not as high as the monthly scale performance, with the accuracy of R2 = 0.636~0.737, RMSE = 14.543–16.916 ppb, MAE = 11.130–12.687 ppb. In general, the trained module based on LSTM is robust and can capture the variation of the atmospheric ozone distribution. Moreover, it also contributes to our understanding of the mechanism of air pollution, especially increasing our comprehension of pollutant areas. View Full-Text
Keywords: lower-stratosphere-to-troposphere; ozone profile; ERA5; satellite data; LSTM lower-stratosphere-to-troposphere; ozone profile; ERA5; satellite data; LSTM
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MDPI and ACS Style

Zhang, X.; Zhang, Y.; Lu, X.; Bai, L.; Chen, L.; Tao, J.; Wang, Z.; Zhu, L. Estimation of Lower-Stratosphere-to-Troposphere Ozone Profile Using Long Short-Term Memory (LSTM). Remote Sens. 2021, 13, 1374.

AMA Style

Zhang X, Zhang Y, Lu X, Bai L, Chen L, Tao J, Wang Z, Zhu L. Estimation of Lower-Stratosphere-to-Troposphere Ozone Profile Using Long Short-Term Memory (LSTM). Remote Sensing. 2021; 13(7):1374.

Chicago/Turabian Style

Zhang, Xinxin, Ying Zhang, Xiaoyan Lu, Lu Bai, Liangfu Chen, Jinhua Tao, Zhibao Wang, and Lili Zhu. 2021. "Estimation of Lower-Stratosphere-to-Troposphere Ozone Profile Using Long Short-Term Memory (LSTM)" Remote Sensing 13, no. 7: 1374.

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