Articles | Volume 16, issue 19
https://doi.org/10.5194/amt-16-4643-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-16-4643-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Oct 2023
Research article |
| 12 Oct 2023
| 12 Oct 2023
A research product for tropospheric NO2 columns from Geostationary Environment Monitoring Spectrometer based on Peking University OMI NO2 algorithm
Yuhang Zhang
Laboratory for Climate and Ocean–Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
Laboratory for Climate and Ocean–Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
Jhoon Kim
Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea
Hanlim Lee
Division of Earth Environmental System Science Major of Spatial Information Engineering, Pukyong National University, Busan, South Korea
Junsung Park
Division of Earth Environmental System Science Major of Spatial Information Engineering, Pukyong National University, Busan, South Korea
Hyunkee Hong
National Institute of Environmental Research, Incheon, South Korea
Michel Van Roozendael
Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium
Francois Hendrick
Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium
Ting Wang
CNRC & LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
University of Chinese Academy of Sciences, Beijing 100049, China
Pucai Wang
CNRC & LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
University of Chinese Academy of Sciences, Beijing 100049, China
School of Environment and Geoinformatics, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
School of Environment and Geoinformatics, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
Yongjoo Choi
Department of Environmental Science, Hankuk University of Foreign Studies, Yongin, South Korea
Yugo Kanaya
Research Institute for Global Change, Japan Agency for Marine–Earth Science and Technology (JAMSTEC), Yokohama 2360001, Japan
Jin Xu
Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Science, Hefei 230031, China
Pinhua Xie
University of Chinese Academy of Sciences, Beijing 100049, China
Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Science, Hefei 230031, China
Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China
Sanbao Zhang
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
Shanshan Wang
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
Siyang Cheng
State Key Laboratory of Severe Weather & Institute of Tibetan Plateau Meteorology, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Xinghong Cheng
State Key Laboratory of Severe Weather & Institute of Tibetan Plateau Meteorology, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Jianzhong Ma
State Key Laboratory of Severe Weather & Institute of Tibetan Plateau Meteorology, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Thomas Wagner
Satellite Remote Sensing, Max Planck Institute for Chemistry, 55020 Mainz, Germany
Robert Spurr
RT Solutions Inc., Cambridge, MA 02138, USA
Lulu Chen
College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Hao Kong
Laboratory for Climate and Ocean–Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
Mengyao Liu
R&D Satellite Observations Department, Royal Netherlands Meteorological Institute, De Bilt, the Netherlands
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Cited
8 citations as recorded by crossref.
- Deep learning bias correction of GEMS tropospheric NO2: A comparative validation of NO2 from GEMS and TROPOMI using Pandora observations M. Ghahremanloo et al. 10.1016/j.envint.2024.108818
- Quantifying the diurnal variation in atmospheric NO2 from Geostationary Environment Monitoring Spectrometer (GEMS) observations D. Edwards et al. 10.5194/acp-24-8943-2024
- Tropospheric NO2 retrieval algorithm for geostationary satellite instruments: applications to GEMS S. Seo et al. 10.5194/amt-17-6163-2024
- Estimation of ground-level NO2 and its spatiotemporal variations in China using GEMS measurements and a nested machine learning model N. Ahmad et al. 10.5194/acp-24-9645-2024
- A bias-corrected GEMS geostationary satellite product for nitrogen dioxide using machine learning to enforce consistency with the TROPOMI satellite instrument Y. Oak et al. 10.5194/amt-17-5147-2024
- Validation of GEMS tropospheric NO2 columns and their diurnal variation with ground-based DOAS measurements K. Lange et al. 10.5194/amt-17-6315-2024
- Reply to: NO2 satellite retrievals biased by absorption in water H. Kong et al. 10.1038/s41561-024-01546-7
- Retrieval of Tropospheric NO2 Vertical Column Densities from Ground-Based MAX-DOAS Measurements in Lhasa, a City on the Tibetan Plateau S. Cheng et al. 10.3390/rs15194689
7 citations as recorded by crossref.
- Deep learning bias correction of GEMS tropospheric NO2: A comparative validation of NO2 from GEMS and TROPOMI using Pandora observations M. Ghahremanloo et al. 10.1016/j.envint.2024.108818
- Quantifying the diurnal variation in atmospheric NO2 from Geostationary Environment Monitoring Spectrometer (GEMS) observations D. Edwards et al. 10.5194/acp-24-8943-2024
- Tropospheric NO2 retrieval algorithm for geostationary satellite instruments: applications to GEMS S. Seo et al. 10.5194/amt-17-6163-2024
- Estimation of ground-level NO2 and its spatiotemporal variations in China using GEMS measurements and a nested machine learning model N. Ahmad et al. 10.5194/acp-24-9645-2024
- A bias-corrected GEMS geostationary satellite product for nitrogen dioxide using machine learning to enforce consistency with the TROPOMI satellite instrument Y. Oak et al. 10.5194/amt-17-5147-2024
- Validation of GEMS tropospheric NO2 columns and their diurnal variation with ground-based DOAS measurements K. Lange et al. 10.5194/amt-17-6315-2024
- Reply to: NO2 satellite retrievals biased by absorption in water H. Kong et al. 10.1038/s41561-024-01546-7
Latest update: 22 Nov 2024
Short summary
Our tropospheric NO2 vertical column density product with high spatiotemporal resolution is based on the Geostationary Environment Monitoring Spectrometer (GEMS) and named POMINO–GEMS. Strong hotspot signals and NO2 diurnal variations are clearly seen. Validations with multiple satellite products and ground-based, mobile car and surface measurements exhibit the overall great performance of the POMINO–GEMS product, indicating its capability for application in environmental studies.
Our tropospheric NO2 vertical column density product with high spatiotemporal resolution is...
Special issue