Articles | Volume 15, issue 10
https://doi.org/10.5194/gmd-15-4225-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-4225-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
01 Jun 2022
Model evaluation paper |
| 01 Jun 2022
| 01 Jun 2022
A comparative analysis for a deep learning model (hyDL-CO v1.0) and Kalman filter to predict CO concentrations in China
Weichao Han
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui, 230026, China
Tai-Long He
Department of Physics, University of Toronto, Toronto, ON, M5S 1A7, Canada
Zhaojun Tang
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui, 230026, China
Min Wang
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui, 230026, China
Dylan Jones
Department of Physics, University of Toronto, Toronto, ON, M5S 1A7, Canada
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui, 230026, China
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Cited
16 citations as recorded by crossref.
- Meteorological and anthropogenic drivers of surface ozone change in the North China Plain in 2015–2021 M. Wang et al. https://doi.org/10.1016/j.scitotenv.2023.167763
- Inverse modelling of Chinese NOx emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis T. He et al. https://doi.org/10.5194/acp-22-14059-2022
- Data‐ and Model‐Based Urban O3 Responses to NOx Changes in China and the United States X. Chen et al. https://doi.org/10.1029/2022JD038228
- Daily seamless dataset of HCHO concentrations: Vertical relationship between surface and column HCHO in China in 2019–2022 M. Wang et al. https://doi.org/10.1016/j.atmosenv.2025.121546
- Spatial and seasonal variations and trends in carbon monoxide over China during 2013–2022 Y. Xie et al. https://doi.org/10.1016/j.atmosenv.2025.121163
- Rapid O3 assimilations – Part 2: Tropospheric O3 changes accompanied by declining NOx emissions in the USA and Europe in 2005–2020 R. Zhu et al. https://doi.org/10.5194/acp-23-9745-2023
- A novel ensemble machine learning exposure model system for ground-level ozone at the national scale: A case of mainland China from 2013 to 2020 J. Wang https://doi.org/10.1016/j.eiar.2024.107630
- Potential of deep learning in inference of atmospheric pollutant emissions L. Sun et al. https://doi.org/10.1016/j.atmosenv.2026.122007
- The Capability of Deep Learning Model to Predict Ozone Across Continents in China, the United States and Europe W. Han et al. https://doi.org/10.1029/2023GL104928
- Discrepancy in assimilated atmospheric CO over East Asia in 2015–2020 by assimilating satellite and surface CO measurements Z. Tang et al. https://doi.org/10.5194/acp-22-7815-2022
- Trifluoroacetic Acid (TFA) Deposition and Accumulation Potential from Future Emissions of HFO-1234yf in China Y. Wang et al. https://doi.org/10.1021/acs.est.5c18352
- Quantifying and correcting systematic discrepancies in the comparison between surface CO observations and simulations C. Fang et al. https://doi.org/10.1016/j.atmosenv.2025.121769
- Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations J. Wei et al. https://doi.org/10.5194/acp-23-1511-2023
- Rapid O3 assimilations – Part 1: Background and local contributions to tropospheric O3 changes in China in 2015–2020 R. Zhu et al. https://doi.org/10.5194/gmd-16-6337-2023
- Development of an integrated machine learning model to improve the secondary inorganic aerosol simulation over the Beijing–Tianjin–Hebei region N. Ding et al. https://doi.org/10.1016/j.atmosenv.2024.120483
- Integrating geostatistical methods and deep learning for enhanced 87Sr/86Sr isoscape Estimation: A case study in South Korea H. Lee et al. https://doi.org/10.1016/j.jag.2025.104697
16 citations as recorded by crossref.
- Meteorological and anthropogenic drivers of surface ozone change in the North China Plain in 2015–2021 M. Wang et al. https://doi.org/10.1016/j.scitotenv.2023.167763
- Inverse modelling of Chinese NOx emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis T. He et al. https://doi.org/10.5194/acp-22-14059-2022
- Data‐ and Model‐Based Urban O3 Responses to NOx Changes in China and the United States X. Chen et al. https://doi.org/10.1029/2022JD038228
- Daily seamless dataset of HCHO concentrations: Vertical relationship between surface and column HCHO in China in 2019–2022 M. Wang et al. https://doi.org/10.1016/j.atmosenv.2025.121546
- Spatial and seasonal variations and trends in carbon monoxide over China during 2013–2022 Y. Xie et al. https://doi.org/10.1016/j.atmosenv.2025.121163
- Rapid O3 assimilations – Part 2: Tropospheric O3 changes accompanied by declining NOx emissions in the USA and Europe in 2005–2020 R. Zhu et al. https://doi.org/10.5194/acp-23-9745-2023
- A novel ensemble machine learning exposure model system for ground-level ozone at the national scale: A case of mainland China from 2013 to 2020 J. Wang https://doi.org/10.1016/j.eiar.2024.107630
- Potential of deep learning in inference of atmospheric pollutant emissions L. Sun et al. https://doi.org/10.1016/j.atmosenv.2026.122007
- The Capability of Deep Learning Model to Predict Ozone Across Continents in China, the United States and Europe W. Han et al. https://doi.org/10.1029/2023GL104928
- Discrepancy in assimilated atmospheric CO over East Asia in 2015–2020 by assimilating satellite and surface CO measurements Z. Tang et al. https://doi.org/10.5194/acp-22-7815-2022
- Trifluoroacetic Acid (TFA) Deposition and Accumulation Potential from Future Emissions of HFO-1234yf in China Y. Wang et al. https://doi.org/10.1021/acs.est.5c18352
- Quantifying and correcting systematic discrepancies in the comparison between surface CO observations and simulations C. Fang et al. https://doi.org/10.1016/j.atmosenv.2025.121769
- Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations J. Wei et al. https://doi.org/10.5194/acp-23-1511-2023
- Rapid O3 assimilations – Part 1: Background and local contributions to tropospheric O3 changes in China in 2015–2020 R. Zhu et al. https://doi.org/10.5194/gmd-16-6337-2023
- Development of an integrated machine learning model to improve the secondary inorganic aerosol simulation over the Beijing–Tianjin–Hebei region N. Ding et al. https://doi.org/10.1016/j.atmosenv.2024.120483
- Integrating geostatistical methods and deep learning for enhanced 87Sr/86Sr isoscape Estimation: A case study in South Korea H. Lee et al. https://doi.org/10.1016/j.jag.2025.104697
Saved (final revised paper)
Latest update: 11 Jun 2026
Short summary
We present an application of a hybrid deep learning (DL) model on prediction of surface CO in China from 2015 to 2020, which utilizes both convolutional neural networks and long short-term memory neural networks. The DL model performance is better than a Kalman filter (KF) system in the training period (2005–2018). Furthermore, the DL model demonstrates good temporal extensibility: the mean bias and correlation coefficients are 95.7 ppb and 0.93 in the test period (2019–2020) over eastern China.
We present an application of a hybrid deep learning (DL) model on prediction of surface CO in...
