Articles | Volume 25, issue 15
https://doi.org/10.5194/acp-25-8507-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-8507-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Aug 2025
Research article |
| 06 Aug 2025
| 06 Aug 2025
Identifying drivers of surface ozone bias in global chemical reanalysis with explainable machine learning
Kazuyuki Miyazaki
CORRESPONDING AUTHOR
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Yuliya Marchetti
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
James Montgomery
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Steven Lu
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Kevin Bowman
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Related authors
Dylan B. A. Jones, Lucas Prates, Zhen Qu, William Y. Y. Cheng, Kazuyuki Miyazaki, Takashi Sekiya, Antje Inness, Rajesh Kumar, Xiao Tang, Helen Worden, Gerbrand Koren, and Vincent Huijnen
Atmos. Chem. Phys., 26, 6655–6682, https://doi.org/10.5194/acp-26-6655-2026, https://doi.org/10.5194/acp-26-6655-2026, 2026
Short summary
Short summary
We evaluate five chemical reanalysis products to assess their potential to provide useful information on tropospheric ozone variability. We find that the reanalyses produce consistent information on ozone variations in the free troposphere, but have large discrepancies at the surface. The results suggests that improvements in the reanalyses are needed to better exploit the assimilated observations to enhance the utility of the reanalysis products at the surface.
Jinkyul Choi, Kazuyuki Miyazaki, Takashi Sekiya, Jinhyeok Yu, Shin-Ya Ogino, Henk Eskes, Pieter Rijdijk, Ryan Bennett, Anke Roiger, Leon Knez, Glenn S. Diskin, Jason A. Miech, Joshua P. DiGangi, Yonghoon Choi, Alessandro Franchin, Changmin Cho, Eric C. Apel, Louisa K. Emmons, Nattamon Maneenoi, Jason M. St. Clair, Reem A. Hannun, Glenn M. Wolfe, Erin Delaria, Abby Sebol, Donald R. Blake, Monica Crippa, Rachel M. Hoesly, Steven J. Smith, Jung-Hun Woo, Le Kuai, and Ronald Macatangay
EGUsphere, https://doi.org/10.5194/egusphere-2026-2380, https://doi.org/10.5194/egusphere-2026-2380, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Asia produces half of the world's air pollution, yet emission estimates from different sources disagree widely. We assimilated satellite observations of six pollutant species to correct both concentrations and emissions. Aircraft measurements from a 2024 NASA campaign across four Asian countries confirmed the improvements. Our results reveal underreported emissions in some countries, overestimated vegetation emissions, unresolved fire estimates, and weather control on cross-border pollution.
Takashi Sekiya, Kazuyuki Miyazaki, Henk Eskes, Pieter Rijsdijk, Kengo Sudo, and Yugo Kanaya
EGUsphere, https://doi.org/10.5194/egusphere-2026-1681, https://doi.org/10.5194/egusphere-2026-1681, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
This study evaluates the synergistic effects of satellite aerosol optical depth (AOD) and trace gas data assimilation on aerosol composition analyses. The simultaneous data assimilation (DA) improved the PM2.5 model biases in Northeast Asia compared to conventional DA run that separately assimilate AOD satellite observations. This coupled aerosol and trace gas DA framework offers significant advantages for improving global aerosol composition analyses.
Zhaojun Tang, Panpan Yang, Kazuyuki Miyazaki, John Worden, Helen Worden, Daven K. Henze, Dylan B. A. Jones, and Zhe Jiang
Atmos. Chem. Phys., 26, 5531–5551, https://doi.org/10.5194/acp-26-5531-2026, https://doi.org/10.5194/acp-26-5531-2026, 2026
Short summary
Short summary
We provide a quantitative analysis of global CO emissions and drivers of atmospheric CO trends in 2003–2022, using GEOS-Chem adjoint model constrained by satellite CO measurements. Our results indicate a substantial decline in global anthropogenic CO emissions of 14 %–17 % over the two-decade period, as well as an important offsetting effect from biomass burning emissions on atmospheric CO decline driven by anthropogenic reductions, primarily attributable to extreme wildfire events in 2021.
Sachiko Okamoto, Juan Cuesta, Gaëlle Dufour, Maxim Eremenko, Kazuyuki Miyazaki, Cathy Boonne, Hiroshi Tanimoto, Jeff Peischl, and Chelsea Thompson
Atmos. Chem. Phys., 26, 4685–4709, https://doi.org/10.5194/acp-26-4685-2026, https://doi.org/10.5194/acp-26-4685-2026, 2026
Short summary
Short summary
We analyse the distribution of tropospheric ozone over the Atlantic during February and October 2017 using a multispectral satellite synergism called IASI+GOME2, two chemistry reanalysis products and in situ airborne measurements. It reveals that a significant overestimation of two chemistry reanalysis products of lowermost troposphere ozone over the Atlantic in the Northern Hemisphere due to the overestimations of ozone precursors from anthropogenic sources from North America.
Julianna Antonia Christopoulos, Pablo Saide, Manas Mohanty, Nattamon Maneenoi, Jhoon Kim, Laura Judd, Katherine Travis, Savitri Garivait, Agapol Junpen, Kazuyuki Miyazaki, Jinkyul Choi, Takashi Sekiya, David Peterson, Theodore McHardy, Nicholas Gapp, Jason St. Clair, Erin Delaria, Glenn Wolfe, Abby Sebol, Alessandro Franchin, Changmin Cho, Morgan Silverman, and James H. Crawford
EGUsphere, https://doi.org/10.5194/egusphere-2026-472, https://doi.org/10.5194/egusphere-2026-472, 2026
Short summary
Short summary
Air quality in rapidly growing cities is difficult to model because emissions vary throughout the day and are poorly constrained. We study Bangkok by combining hourly satellite observations with high-resolution air quality modeling to estimate nitrogen dioxide emissions. We find emissions are much lower than commonly used inventories, and that updated emissions improve model agreement with aircraft and ground observations.
Sebastian H. M. Hickman, Makoto M. Kelp, Paul T. Griffiths, Kelsey Doerksen, Kazuyuki Miyazaki, Elyse A. Pennington, Gerbrand Koren, Fernando Iglesias-Suarez, Martin G. Schultz, Kai-Lan Chang, Owen R. Cooper, Alex Archibald, Roberto Sommariva, David Carlson, Hantao Wang, J. Jason West, and Zhenze Liu
Geosci. Model Dev., 18, 8777–8800, https://doi.org/10.5194/gmd-18-8777-2025, https://doi.org/10.5194/gmd-18-8777-2025, 2025
Short summary
Short summary
Machine learning is being more widely used across environmental and climate science. This work reviews the use of machine learning in tropospheric ozone research, focusing on three main application areas in which significant progress has been made. Common challenges in using machine learning across the three areas are highlighted, and future directions for the field are indicated.
Hantao Wang, Kazuyuki Miyazaki, Haitong Zhe Sun, Zhen Qu, Xiang Liu, Antje Inness, Martin Schultz, Sabine Schröder, Marc Serre, and J. Jason West
Atmos. Chem. Phys., 25, 15969–15990, https://doi.org/10.5194/acp-25-15969-2025, https://doi.org/10.5194/acp-25-15969-2025, 2025
Short summary
Short summary
We compare six datasets of global ground-level ozone, developed using geostatistical, machine learning, or reanalysis methods. The datasets show important differences from one another in ozone magnitude, greater than 5 ppb, and trends, globally and regionally. Compared with measurements, performance varies among datasets, and most overestimate ozone, particularly at lower concentrations. These differences among datasets highlight uncertainties for applications to health and other impacts.
Elyse A. Pennington, Gregory B. Osterman, Vivienne H. Payne, Kazuyuki Miyazaki, Kevin W. Bowman, and Jessica L. Neu
Atmos. Chem. Phys., 25, 8533–8552, https://doi.org/10.5194/acp-25-8533-2025, https://doi.org/10.5194/acp-25-8533-2025, 2025
Short summary
Short summary
Tropospheric ozone is a harmful pollutant and powerful greenhouse gas. For satellite products to accurately quantify trends in tropospheric ozone, they must have a low bias compared to a reliable source of data. This study compares three NASA satellite products to ozonesonde data. They have low global measurement bias and thus can be used to detect global tropospheric ozone trends, but the measurement bias should be considered in certain regions and time periods.
Mukesh Rai, Kazuyuki Miyazaki, Vivienne Payne, Bin Guan, and Duane Waliser
EGUsphere, https://doi.org/10.5194/egusphere-2025-399, https://doi.org/10.5194/egusphere-2025-399, 2025
Preprint archived
Short summary
Short summary
This study introduces a novel method for quantifying extreme events of trace gas air pollutants by leveraging a tropospheric chemical reanalysis data set. The analysis revealed that while extreme events are infrequent, they contribute substantially (60 %) to the total transport of pollutants. This finding underscores the critical role of long-range transport events in determining global and regional air quality.
Takashi Sekiya, Emanuele Emili, Kazuyuki Miyazaki, Antje Inness, Zhen Qu, R. Bradley Pierce, Dylan Jones, Helen Worden, William Y. Y. Cheng, Vincent Huijnen, and Gerbrand Koren
Atmos. Chem. Phys., 25, 2243–2268, https://doi.org/10.5194/acp-25-2243-2025, https://doi.org/10.5194/acp-25-2243-2025, 2025
Short summary
Short summary
Five global chemical reanalysis datasets were used to assess the relative impacts of assimilating satellite ozone and its precursor measurements on tropospheric ozone analyses for 2010. The multiple reanalysis system comparison allows an evaluation of the dependency of the impacts on different reanalysis systems. The results suggested the importance of satellite ozone and its precursor measurements for improving ozone analysis in the whole troposphere, with varying magnitudes among the systems.
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025, https://doi.org/10.5194/gmd-18-483-2025, 2025
Short summary
Short summary
Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.
Yasin Elshorbany, Jerald R. Ziemke, Sarah Strode, Hervé Petetin, Kazuyuki Miyazaki, Isabelle De Smedt, Kenneth Pickering, Rodrigo J. Seguel, Helen Worden, Tamara Emmerichs, Domenico Taraborrelli, Maria Cazorla, Suvarna Fadnavis, Rebecca R. Buchholz, Benjamin Gaubert, Néstor Y. Rojas, Thiago Nogueira, Thérèse Salameh, and Min Huang
Atmos. Chem. Phys., 24, 12225–12257, https://doi.org/10.5194/acp-24-12225-2024, https://doi.org/10.5194/acp-24-12225-2024, 2024
Short summary
Short summary
We investigated tropospheric ozone spatial variability and trends from 2005 to 2019 and related those to ozone precursors on global and regional scales. We also investigate the spatiotemporal characteristics of the ozone formation regime in relation to ozone chemical sources and sinks. Our analysis is based on remote sensing products of the tropospheric column of ozone and its precursors, nitrogen dioxide, formaldehyde, and total column CO, as well as ozonesonde data and model simulations.
Edward Malina, Kevin W. Bowman, Valentin Kantchev, Le Kuai, Thomas P. Kurosu, Kazuyuki Miyazaki, Vijay Natraj, Gregory B. Osterman, Fabiano Oyafuso, and Matthew D. Thill
Atmos. Meas. Tech., 17, 5341–5371, https://doi.org/10.5194/amt-17-5341-2024, https://doi.org/10.5194/amt-17-5341-2024, 2024
Short summary
Short summary
Characterizing the distribution of ozone in the atmosphere is a challenging problem, with current Earth observation satellites using either thermal infrared (TIR) or ultraviolet (UV) instruments, sensitive to different portions of the atmosphere, making it difficult to gain a full picture. In this work, we combine measurements from the TIR and UV instruments Suomi NPP CrIS and Sentinel-5P/TROPOMI to improve sensitivity through the whole atmosphere and improve knowledge of ozone distribution.
Drew C. Pendergrass, Daniel J. Jacob, Hannah Nesser, Daniel J. Varon, Melissa Sulprizio, Kazuyuki Miyazaki, and Kevin W. Bowman
Geosci. Model Dev., 16, 4793–4810, https://doi.org/10.5194/gmd-16-4793-2023, https://doi.org/10.5194/gmd-16-4793-2023, 2023
Short summary
Short summary
We have built a tool called CHEEREIO that allows scientists to use observations of pollutants or gases in the atmosphere, such as from satellites or surface stations, to update supercomputer models that simulate the Earth. CHEEREIO uses the difference between the model simulations of the atmosphere and real-world observations to come up with a good guess for the actual composition of our atmosphere, the true emissions of various pollutants, and whatever else they may want to study.
Sachiko Okamoto, Juan Cuesta, Matthias Beekmann, Gaëlle Dufour, Maxim Eremenko, Kazuyuki Miyazaki, Cathy Boonne, Hiroshi Tanimoto, and Hajime Akimoto
Atmos. Chem. Phys., 23, 7399–7423, https://doi.org/10.5194/acp-23-7399-2023, https://doi.org/10.5194/acp-23-7399-2023, 2023
Short summary
Short summary
We present a detailed analysis of the daily evolution of the lowermost tropospheric ozone documented by IASI+GOME2 multispectral satellite observations and that of its precursors from TCR-2 tropospheric chemistry reanalysis. It reveals that the ozone outbreak across Europe in July 2017 was produced during favorable condition for photochemical production of ozone and was associated with multiple sources of ozone precursors: biogenic, anthropogenic, and biomass burning emissions.
Jagat S. H. Bisht, Prabir K. Patra, Masayuki Takigawa, Takashi Sekiya, Yugo Kanaya, Naoko Saitoh, and Kazuyuki Miyazaki
Geosci. Model Dev., 16, 1823–1838, https://doi.org/10.5194/gmd-16-1823-2023, https://doi.org/10.5194/gmd-16-1823-2023, 2023
Short summary
Short summary
In this study, we estimated CH4 fluxes using an advanced 4D-LETKF method. The system was tested and optimized using observation system simulation experiments (OSSEs), where a known surface emission distribution is retrieved from synthetic observations. The availability of satellite measurements has increased, and there are still many missions focused on greenhouse gas observations that have not yet launched. The technique being referred to has the potential to improve estimates of CH4 fluxes.
Madison J. Shogrin, Vivienne H. Payne, Susan S. Kulawik, Kazuyuki Miyazaki, and Emily V. Fischer
Atmos. Chem. Phys., 23, 2667–2682, https://doi.org/10.5194/acp-23-2667-2023, https://doi.org/10.5194/acp-23-2667-2023, 2023
Short summary
Short summary
We evaluate the spatiotemporal variability of peroxy acyl nitrates (PANs), important photochemical pollutants, over Mexico City using satellite observations. PANs exhibit a seasonal cycle that maximizes in spring. Wildfires contribute to observed interannual variability, and the satellite indicates several areas of frequent outflow. Recent changes in NOx emissions are not accompanied by changes in PANs. This work demonstrates analysis approaches that can be applied to other megacities.
Tai-Long He, Dylan B. A. Jones, Kazuyuki Miyazaki, Kevin W. Bowman, Zhe Jiang, Xiaokang Chen, Rui Li, Yuxiang Zhang, and Kunna Li
Atmos. Chem. Phys., 22, 14059–14074, https://doi.org/10.5194/acp-22-14059-2022, https://doi.org/10.5194/acp-22-14059-2022, 2022
Short summary
Short summary
We use a deep-learning (DL) model to estimate Chinese NOx emissions by combining satellite analysis and in situ measurements. Our results are consistent with conventional analyses of Chinese NOx emissions. Comparison with mobility data shows that the DL model has a better capability to capture changes in NOx. We analyse Chinese NOx emissions during the COVID-19 pandemic lockdown period. Our results illustrate the potential use of DL as a complementary tool for conventional air quality studies.
Vivienne H. Payne, Susan S. Kulawik, Emily V. Fischer, Jared F. Brewer, L. Gregory Huey, Kazuyuki Miyazaki, John R. Worden, Kevin W. Bowman, Eric J. Hintsa, Fred Moore, James W. Elkins, and Julieta Juncosa Calahorrano
Atmos. Meas. Tech., 15, 3497–3511, https://doi.org/10.5194/amt-15-3497-2022, https://doi.org/10.5194/amt-15-3497-2022, 2022
Short summary
Short summary
We compare new satellite measurements of peroxyacetyl nitrate (PAN) with reference aircraft measurements from two different instruments flown on the same platform. While there is a systematic difference between the two aircraft datasets, both show the same large-scale distribution of PAN and the discrepancy between aircraft datasets is small compared to the satellite uncertainties. The satellite measurements show skill in capturing large-scale variations in PAN.
Takashi Sekiya, Kazuyuki Miyazaki, Henk Eskes, Kengo Sudo, Masayuki Takigawa, and Yugo Kanaya
Atmos. Meas. Tech., 15, 1703–1728, https://doi.org/10.5194/amt-15-1703-2022, https://doi.org/10.5194/amt-15-1703-2022, 2022
Short summary
Short summary
This study gives a systematic comparison of TROPOMI version 1.2 and OMI QA4ECV tropospheric NO2 column through global chemical data assimilation (DA) integration for April–May 2018. DA performance is controlled by measurement sensitivities, retrieval errors, and coverage. Due to reduced errors in TROPOMI, agreements against assimilated and independent observations were improved by TROPOMI DA compared to OMI DA. These results demonstrate that TROPOMI DA improves global analyses of NO2 and ozone.
Zhe Jiang, Hongrong Shi, Bin Zhao, Yu Gu, Yifang Zhu, Kazuyuki Miyazaki, Xin Lu, Yuqiang Zhang, Kevin W. Bowman, Takashi Sekiya, and Kuo-Nan Liou
Atmos. Chem. Phys., 21, 8693–8708, https://doi.org/10.5194/acp-21-8693-2021, https://doi.org/10.5194/acp-21-8693-2021, 2021
Short summary
Short summary
We use the COVID-19 pandemic as a unique natural experiment to obtain a more robust understanding of the effectiveness of emission reductions toward air quality improvement by combining chemical transport simulations and observations. Our findings imply a shift from current control policies in California: a strengthened control on primary PM2.5 emissions and a well-balanced control on NOx and volatile organic compounds are needed to effectively and sustainably alleviate PM2.5 and O3 pollution.
Dylan B. A. Jones, Lucas Prates, Zhen Qu, William Y. Y. Cheng, Kazuyuki Miyazaki, Takashi Sekiya, Antje Inness, Rajesh Kumar, Xiao Tang, Helen Worden, Gerbrand Koren, and Vincent Huijnen
Atmos. Chem. Phys., 26, 6655–6682, https://doi.org/10.5194/acp-26-6655-2026, https://doi.org/10.5194/acp-26-6655-2026, 2026
Short summary
Short summary
We evaluate five chemical reanalysis products to assess their potential to provide useful information on tropospheric ozone variability. We find that the reanalyses produce consistent information on ozone variations in the free troposphere, but have large discrepancies at the surface. The results suggests that improvements in the reanalyses are needed to better exploit the assimilated observations to enhance the utility of the reanalysis products at the surface.
Frank Werner, Kevin W. Bowman, Seungwon Lee, Joshua L. Laughner, Vivienne H. Payne, and James L. McDuffie
Atmos. Meas. Tech., 19, 3095–3109, https://doi.org/10.5194/amt-19-3095-2026, https://doi.org/10.5194/amt-19-3095-2026, 2026
Short summary
Short summary
We developed a hybrid machine learning-optimal estimation retrieval system that efficiently and accurately mimics operational retrieval results. Crucially, this algorithm also predicts critical diagnostic variables including observation operators needed for comparison with independent data and ingestion into downstream chemical data assimilation models.
Jinkyul Choi, Kazuyuki Miyazaki, Takashi Sekiya, Jinhyeok Yu, Shin-Ya Ogino, Henk Eskes, Pieter Rijdijk, Ryan Bennett, Anke Roiger, Leon Knez, Glenn S. Diskin, Jason A. Miech, Joshua P. DiGangi, Yonghoon Choi, Alessandro Franchin, Changmin Cho, Eric C. Apel, Louisa K. Emmons, Nattamon Maneenoi, Jason M. St. Clair, Reem A. Hannun, Glenn M. Wolfe, Erin Delaria, Abby Sebol, Donald R. Blake, Monica Crippa, Rachel M. Hoesly, Steven J. Smith, Jung-Hun Woo, Le Kuai, and Ronald Macatangay
EGUsphere, https://doi.org/10.5194/egusphere-2026-2380, https://doi.org/10.5194/egusphere-2026-2380, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Asia produces half of the world's air pollution, yet emission estimates from different sources disagree widely. We assimilated satellite observations of six pollutant species to correct both concentrations and emissions. Aircraft measurements from a 2024 NASA campaign across four Asian countries confirmed the improvements. Our results reveal underreported emissions in some countries, overestimated vegetation emissions, unresolved fire estimates, and weather control on cross-border pollution.
Takashi Sekiya, Kazuyuki Miyazaki, Henk Eskes, Pieter Rijsdijk, Kengo Sudo, and Yugo Kanaya
EGUsphere, https://doi.org/10.5194/egusphere-2026-1681, https://doi.org/10.5194/egusphere-2026-1681, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
This study evaluates the synergistic effects of satellite aerosol optical depth (AOD) and trace gas data assimilation on aerosol composition analyses. The simultaneous data assimilation (DA) improved the PM2.5 model biases in Northeast Asia compared to conventional DA run that separately assimilate AOD satellite observations. This coupled aerosol and trace gas DA framework offers significant advantages for improving global aerosol composition analyses.
Zhaojun Tang, Panpan Yang, Kazuyuki Miyazaki, John Worden, Helen Worden, Daven K. Henze, Dylan B. A. Jones, and Zhe Jiang
Atmos. Chem. Phys., 26, 5531–5551, https://doi.org/10.5194/acp-26-5531-2026, https://doi.org/10.5194/acp-26-5531-2026, 2026
Short summary
Short summary
We provide a quantitative analysis of global CO emissions and drivers of atmospheric CO trends in 2003–2022, using GEOS-Chem adjoint model constrained by satellite CO measurements. Our results indicate a substantial decline in global anthropogenic CO emissions of 14 %–17 % over the two-decade period, as well as an important offsetting effect from biomass burning emissions on atmospheric CO decline driven by anthropogenic reductions, primarily attributable to extreme wildfire events in 2021.
Sachiko Okamoto, Juan Cuesta, Gaëlle Dufour, Maxim Eremenko, Kazuyuki Miyazaki, Cathy Boonne, Hiroshi Tanimoto, Jeff Peischl, and Chelsea Thompson
Atmos. Chem. Phys., 26, 4685–4709, https://doi.org/10.5194/acp-26-4685-2026, https://doi.org/10.5194/acp-26-4685-2026, 2026
Short summary
Short summary
We analyse the distribution of tropospheric ozone over the Atlantic during February and October 2017 using a multispectral satellite synergism called IASI+GOME2, two chemistry reanalysis products and in situ airborne measurements. It reveals that a significant overestimation of two chemistry reanalysis products of lowermost troposphere ozone over the Atlantic in the Northern Hemisphere due to the overestimations of ozone precursors from anthropogenic sources from North America.
Lucas A. Estrada, Daniel J. Jacob, Megan He, James D. East, Daniel J. Varon, Nicholas Balasus, Sarah E. Hancock, Melissa Sulprizio, Kevin W. Bowman, John R. Worden, Emily Reidy, and Benjamin R. K. Runkle
EGUsphere, https://doi.org/10.5194/egusphere-2026-655, https://doi.org/10.5194/egusphere-2026-655, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We use satellite observations to track U.S. methane emissions from 2019–2024 at national, state, and oil and gas field scales. While total emissions remain stable, oil and gas emissions decline even as production rises, indicating progress in emissions management for the oil and gas sector.
John Worden, Sudhanshu Pandey, Hannah Nesser, Kevin Bowman, Colin Harkins, Congmeng Lyu, Joannes D. Maasakkers, Deborah Gordon, Daniel Jacob, Lucas Estrada, Daniel J. Varon, James D. East, Lauren Schmeisser, and Zhen Qu
EGUsphere, https://doi.org/10.5194/egusphere-2026-313, https://doi.org/10.5194/egusphere-2026-313, 2026
Short summary
Short summary
To support methane reduction, we compared three widely used maps of methane emissions in the United States with estimates derived from a methane-measuring satellite from 2012 to 2020. The satellite indicates higher emissions from oil and gas production and from livestock in several regions, while other sources were harder to pin down. Neither approach shows a clear change over time. The results point to where better measurements can most improve emissions reporting and guide mitigation.
Julianna Antonia Christopoulos, Pablo Saide, Manas Mohanty, Nattamon Maneenoi, Jhoon Kim, Laura Judd, Katherine Travis, Savitri Garivait, Agapol Junpen, Kazuyuki Miyazaki, Jinkyul Choi, Takashi Sekiya, David Peterson, Theodore McHardy, Nicholas Gapp, Jason St. Clair, Erin Delaria, Glenn Wolfe, Abby Sebol, Alessandro Franchin, Changmin Cho, Morgan Silverman, and James H. Crawford
EGUsphere, https://doi.org/10.5194/egusphere-2026-472, https://doi.org/10.5194/egusphere-2026-472, 2026
Short summary
Short summary
Air quality in rapidly growing cities is difficult to model because emissions vary throughout the day and are poorly constrained. We study Bangkok by combining hourly satellite observations with high-resolution air quality modeling to estimate nitrogen dioxide emissions. We find emissions are much lower than commonly used inventories, and that updated emissions improve model agreement with aircraft and ground observations.
Raphaël Savelli, Dustin Carroll, Dimitris Menemenlis, Jonathan M. Lauderdale, Clément Bertin, Stephanie Dutkiewicz, Manfredi Manizza, A. Anthony Bloom, Karel Castro-Morales, Charles E. Miller, Marc Simard, Kevin W. Bowman, and Hong Zhang
Geosci. Model Dev., 19, 867–885, https://doi.org/10.5194/gmd-19-867-2026, https://doi.org/10.5194/gmd-19-867-2026, 2026
Short summary
Short summary
Accounting for carbon and nutrients in rivers is essential for resolving carbon dioxide (CO2) exchanges between the ocean and the atmosphere. In this study, we add the effect of present-day rivers to a pioneering global-ocean biogeochemistry model. This study highlights the challenge for global ocean numerical models to cover the complexity of the flow of water and carbon across the Land-to-Ocean Aquatic Continuum.
Jean-Francois Lamarque, Pierre Friedlingstein, Brian Osias, Steve Strongin, Venkatramani Balaji, Kevin W. Bowman, Josep G. Canadell, Philippe Ciais, Heidi Cullen, Kenneth J. Davis, Scott C. Doney, Kevin R. Gurney, Alicia R. Karspeck, Charles D. Koven, Galen McKinley, Glen P. Peters, Julia Pongratz, Britt Stephens, and Colm Sweeney
EGUsphere, https://doi.org/10.5194/egusphere-2025-6457, https://doi.org/10.5194/egusphere-2025-6457, 2026
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
This Perspective highlights requirements to scale the carbon credit market and enable the growth in climate solutions funded through such market. The requirements are on the understanding of the value of the proposed carbon credit projects, and on the availability of a verification system. This verification becomes particularly relevant as the carbon credit market scales to significant impacts on CO2 (or other greenhouse gases), such that attribution to collective actions can be identified.
Hannah Nesser, Kevin W. Bowman, Matthew D. Thill, Daniel J. Varon, Cynthia A. Randles, Ashutosh Tewari, Felipe J. Cardoso-Saldaña, Emily Reidy, Joannes D. Maasakkers, and Daniel J. Jacob
Geosci. Model Dev., 18, 9279–9291, https://doi.org/10.5194/gmd-18-9279-2025, https://doi.org/10.5194/gmd-18-9279-2025, 2025
Short summary
Short summary
Regional analyses of atmospheric trace gases can improve knowledge of fluxes at high resolution but rely on specified boundary conditions (BCs) at the domain edges. Biases in the often-uncertain BCs propagate to the inferred fluxes. We develop a framework to explain how errors in the BCs influence the optimized fluxes, derive two metrics to estimate this influence, and compare two methods to correct for the biases. We demonstrate correcting BCs directly is more effective at reducing bias.
Sebastian H. M. Hickman, Makoto M. Kelp, Paul T. Griffiths, Kelsey Doerksen, Kazuyuki Miyazaki, Elyse A. Pennington, Gerbrand Koren, Fernando Iglesias-Suarez, Martin G. Schultz, Kai-Lan Chang, Owen R. Cooper, Alex Archibald, Roberto Sommariva, David Carlson, Hantao Wang, J. Jason West, and Zhenze Liu
Geosci. Model Dev., 18, 8777–8800, https://doi.org/10.5194/gmd-18-8777-2025, https://doi.org/10.5194/gmd-18-8777-2025, 2025
Short summary
Short summary
Machine learning is being more widely used across environmental and climate science. This work reviews the use of machine learning in tropospheric ozone research, focusing on three main application areas in which significant progress has been made. Common challenges in using machine learning across the three areas are highlighted, and future directions for the field are indicated.
Hantao Wang, Kazuyuki Miyazaki, Haitong Zhe Sun, Zhen Qu, Xiang Liu, Antje Inness, Martin Schultz, Sabine Schröder, Marc Serre, and J. Jason West
Atmos. Chem. Phys., 25, 15969–15990, https://doi.org/10.5194/acp-25-15969-2025, https://doi.org/10.5194/acp-25-15969-2025, 2025
Short summary
Short summary
We compare six datasets of global ground-level ozone, developed using geostatistical, machine learning, or reanalysis methods. The datasets show important differences from one another in ozone magnitude, greater than 5 ppb, and trends, globally and regionally. Compared with measurements, performance varies among datasets, and most overestimate ozone, particularly at lower concentrations. These differences among datasets highlight uncertainties for applications to health and other impacts.
Anne Boynard, Catherine Wespes, Juliette Hadji-Lazaro, Selviga Sinnathamby, Daniel Hurtmans, Pierre-François Coheur, Marie Doutriaux-Boucher, Jacobus Onderwaater, Wolfgang Steinbrecht, Elyse A. Pennington, Kevin Bowman, and Cathy Clerbaux
Atmos. Chem. Phys., 25, 11719–11755, https://doi.org/10.5194/acp-25-11719-2025, https://doi.org/10.5194/acp-25-11719-2025, 2025
Short summary
Short summary
This study analyzes 16 years of global ozone data to assess its impact on air quality and climate. Using satellite measurements, we observed a global decrease in tropospheric ozone, particularly in tropical and European regions. The drop became more noticeable around 2020, partly due to reduced emissions during the pandemic. The study highlights the need for better data processing and integration to more accurately monitor ozone changes over time.
Elyse A. Pennington, Gregory B. Osterman, Vivienne H. Payne, Kazuyuki Miyazaki, Kevin W. Bowman, and Jessica L. Neu
Atmos. Chem. Phys., 25, 8533–8552, https://doi.org/10.5194/acp-25-8533-2025, https://doi.org/10.5194/acp-25-8533-2025, 2025
Short summary
Short summary
Tropospheric ozone is a harmful pollutant and powerful greenhouse gas. For satellite products to accurately quantify trends in tropospheric ozone, they must have a low bias compared to a reliable source of data. This study compares three NASA satellite products to ozonesonde data. They have low global measurement bias and thus can be used to detect global tropospheric ozone trends, but the measurement bias should be considered in certain regions and time periods.
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, https://doi.org/10.5194/gmd-18-3311-2025, 2025
Short summary
Short summary
Reducing emissions of methane, a powerful greenhouse gas, is a top policy concern for mitigating anthropogenic climate change. The Integrated Methane Inversion (IMI) is an advanced, cloud-based software that translates satellite observations into actionable emissions data. Here we present IMI version 2.0 with vastly expanded capabilities. These updates enable a wider range of scientific and stakeholder applications from individual basin to global scales with continuous emissions monitoring.
Edward Malina, Jure Brence, Jennifer Adams, Jovan Tanevski, Sašo Džeroski, Valentin Kantchev, and Kevin W. Bowman
Atmos. Meas. Tech., 18, 1689–1715, https://doi.org/10.5194/amt-18-1689-2025, https://doi.org/10.5194/amt-18-1689-2025, 2025
Short summary
Short summary
The large fleet of Earth observation satellites in orbit currently generate huge volumes of data, requiring significant computational resources to process these data in a timely manner. We present a method for predicting poor-quality measurements using machine learning. We find that machine learning methods can accurately predict poor-quality measurements and remove them from the processing chain, saving time and computational resources.
Mukesh Rai, Kazuyuki Miyazaki, Vivienne Payne, Bin Guan, and Duane Waliser
EGUsphere, https://doi.org/10.5194/egusphere-2025-399, https://doi.org/10.5194/egusphere-2025-399, 2025
Preprint archived
Short summary
Short summary
This study introduces a novel method for quantifying extreme events of trace gas air pollutants by leveraging a tropospheric chemical reanalysis data set. The analysis revealed that while extreme events are infrequent, they contribute substantially (60 %) to the total transport of pollutants. This finding underscores the critical role of long-range transport events in determining global and regional air quality.
Takashi Sekiya, Emanuele Emili, Kazuyuki Miyazaki, Antje Inness, Zhen Qu, R. Bradley Pierce, Dylan Jones, Helen Worden, William Y. Y. Cheng, Vincent Huijnen, and Gerbrand Koren
Atmos. Chem. Phys., 25, 2243–2268, https://doi.org/10.5194/acp-25-2243-2025, https://doi.org/10.5194/acp-25-2243-2025, 2025
Short summary
Short summary
Five global chemical reanalysis datasets were used to assess the relative impacts of assimilating satellite ozone and its precursor measurements on tropospheric ozone analyses for 2010. The multiple reanalysis system comparison allows an evaluation of the dependency of the impacts on different reanalysis systems. The results suggested the importance of satellite ozone and its precursor measurements for improving ozone analysis in the whole troposphere, with varying magnitudes among the systems.
Min Huang, Gregory R. Carmichael, Kevin W. Bowman, Isabelle De Smedt, Andreas Colliander, Michael H. Cosh, Sujay V. Kumar, Alex B. Guenther, Scott J. Janz, Ryan M. Stauffer, Anne M. Thompson, Niko M. Fedkin, Robert J. Swap, John D. Bolten, and Alicia T. Joseph
Atmos. Chem. Phys., 25, 1449–1476, https://doi.org/10.5194/acp-25-1449-2025, https://doi.org/10.5194/acp-25-1449-2025, 2025
Short summary
Short summary
We use model simulations along with multiplatform, multidisciplinary observations and a range of analysis methods to estimate and understand the distributions, temporal changes, and impacts of reactive nitrogen and ozone over the most populous US region that has undergone significant environmental changes. Deposition, biogenic emissions, and extra-regional sources have been playing increasingly important roles in controlling pollutant budgets in this area as local anthropogenic emissions drop.
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025, https://doi.org/10.5194/gmd-18-483-2025, 2025
Short summary
Short summary
Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.
Yasin Elshorbany, Jerald R. Ziemke, Sarah Strode, Hervé Petetin, Kazuyuki Miyazaki, Isabelle De Smedt, Kenneth Pickering, Rodrigo J. Seguel, Helen Worden, Tamara Emmerichs, Domenico Taraborrelli, Maria Cazorla, Suvarna Fadnavis, Rebecca R. Buchholz, Benjamin Gaubert, Néstor Y. Rojas, Thiago Nogueira, Thérèse Salameh, and Min Huang
Atmos. Chem. Phys., 24, 12225–12257, https://doi.org/10.5194/acp-24-12225-2024, https://doi.org/10.5194/acp-24-12225-2024, 2024
Short summary
Short summary
We investigated tropospheric ozone spatial variability and trends from 2005 to 2019 and related those to ozone precursors on global and regional scales. We also investigate the spatiotemporal characteristics of the ozone formation regime in relation to ozone chemical sources and sinks. Our analysis is based on remote sensing products of the tropospheric column of ozone and its precursors, nitrogen dioxide, formaldehyde, and total column CO, as well as ozonesonde data and model simulations.
Edward Malina, Kevin W. Bowman, Valentin Kantchev, Le Kuai, Thomas P. Kurosu, Kazuyuki Miyazaki, Vijay Natraj, Gregory B. Osterman, Fabiano Oyafuso, and Matthew D. Thill
Atmos. Meas. Tech., 17, 5341–5371, https://doi.org/10.5194/amt-17-5341-2024, https://doi.org/10.5194/amt-17-5341-2024, 2024
Short summary
Short summary
Characterizing the distribution of ozone in the atmosphere is a challenging problem, with current Earth observation satellites using either thermal infrared (TIR) or ultraviolet (UV) instruments, sensitive to different portions of the atmosphere, making it difficult to gain a full picture. In this work, we combine measurements from the TIR and UV instruments Suomi NPP CrIS and Sentinel-5P/TROPOMI to improve sensitivity through the whole atmosphere and improve knowledge of ozone distribution.
Drew C. Pendergrass, Daniel J. Jacob, Hannah Nesser, Daniel J. Varon, Melissa Sulprizio, Kazuyuki Miyazaki, and Kevin W. Bowman
Geosci. Model Dev., 16, 4793–4810, https://doi.org/10.5194/gmd-16-4793-2023, https://doi.org/10.5194/gmd-16-4793-2023, 2023
Short summary
Short summary
We have built a tool called CHEEREIO that allows scientists to use observations of pollutants or gases in the atmosphere, such as from satellites or surface stations, to update supercomputer models that simulate the Earth. CHEEREIO uses the difference between the model simulations of the atmosphere and real-world observations to come up with a good guess for the actual composition of our atmosphere, the true emissions of various pollutants, and whatever else they may want to study.
Sachiko Okamoto, Juan Cuesta, Matthias Beekmann, Gaëlle Dufour, Maxim Eremenko, Kazuyuki Miyazaki, Cathy Boonne, Hiroshi Tanimoto, and Hajime Akimoto
Atmos. Chem. Phys., 23, 7399–7423, https://doi.org/10.5194/acp-23-7399-2023, https://doi.org/10.5194/acp-23-7399-2023, 2023
Short summary
Short summary
We present a detailed analysis of the daily evolution of the lowermost tropospheric ozone documented by IASI+GOME2 multispectral satellite observations and that of its precursors from TCR-2 tropospheric chemistry reanalysis. It reveals that the ozone outbreak across Europe in July 2017 was produced during favorable condition for photochemical production of ozone and was associated with multiple sources of ozone precursors: biogenic, anthropogenic, and biomass burning emissions.
Thomas E. Taylor, Christopher W. O'Dell, David Baker, Carol Bruegge, Albert Chang, Lars Chapsky, Abhishek Chatterjee, Cecilia Cheng, Frédéric Chevallier, David Crisp, Lan Dang, Brian Drouin, Annmarie Eldering, Liang Feng, Brendan Fisher, Dejian Fu, Michael Gunson, Vance Haemmerle, Graziela R. Keller, Matthäus Kiel, Le Kuai, Thomas Kurosu, Alyn Lambert, Joshua Laughner, Richard Lee, Junjie Liu, Lucas Mandrake, Yuliya Marchetti, Gregory McGarragh, Aronne Merrelli, Robert R. Nelson, Greg Osterman, Fabiano Oyafuso, Paul I. Palmer, Vivienne H. Payne, Robert Rosenberg, Peter Somkuti, Gary Spiers, Cathy To, Brad Weir, Paul O. Wennberg, Shanshan Yu, and Jia Zong
Atmos. Meas. Tech., 16, 3173–3209, https://doi.org/10.5194/amt-16-3173-2023, https://doi.org/10.5194/amt-16-3173-2023, 2023
Short summary
Short summary
NASA's Orbiting Carbon Observatory 2 and 3 (OCO-2 and OCO-3, respectively) provide complementary spatiotemporal coverage from a sun-synchronous and precession orbit, respectively. Estimates of total column carbon dioxide (XCO2) derived from the two sensors using the same retrieval algorithm show broad consistency over a 2.5-year overlapping time record. This suggests that data from the two satellites may be used together for scientific analysis.
Jagat S. H. Bisht, Prabir K. Patra, Masayuki Takigawa, Takashi Sekiya, Yugo Kanaya, Naoko Saitoh, and Kazuyuki Miyazaki
Geosci. Model Dev., 16, 1823–1838, https://doi.org/10.5194/gmd-16-1823-2023, https://doi.org/10.5194/gmd-16-1823-2023, 2023
Short summary
Short summary
In this study, we estimated CH4 fluxes using an advanced 4D-LETKF method. The system was tested and optimized using observation system simulation experiments (OSSEs), where a known surface emission distribution is retrieved from synthetic observations. The availability of satellite measurements has increased, and there are still many missions focused on greenhouse gas observations that have not yet launched. The technique being referred to has the potential to improve estimates of CH4 fluxes.
Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra K. Dubey, Sha Feng, Omaira E. García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O'Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023, https://doi.org/10.5194/essd-15-963-2023, 2023
Short summary
Short summary
Changes in the carbon stocks of terrestrial ecosystems result in emissions and removals of CO2. These can be driven by anthropogenic activities (e.g., deforestation), natural processes (e.g., fires) or in response to rising CO2 (e.g., CO2 fertilization). This paper describes a dataset of CO2 emissions and removals derived from atmospheric CO2 observations. This pilot dataset informs current capabilities and future developments towards top-down monitoring and verification systems.
Madison J. Shogrin, Vivienne H. Payne, Susan S. Kulawik, Kazuyuki Miyazaki, and Emily V. Fischer
Atmos. Chem. Phys., 23, 2667–2682, https://doi.org/10.5194/acp-23-2667-2023, https://doi.org/10.5194/acp-23-2667-2023, 2023
Short summary
Short summary
We evaluate the spatiotemporal variability of peroxy acyl nitrates (PANs), important photochemical pollutants, over Mexico City using satellite observations. PANs exhibit a seasonal cycle that maximizes in spring. Wildfires contribute to observed interannual variability, and the satellite indicates several areas of frequent outflow. Recent changes in NOx emissions are not accompanied by changes in PANs. This work demonstrates analysis approaches that can be applied to other megacities.
Tai-Long He, Dylan B. A. Jones, Kazuyuki Miyazaki, Kevin W. Bowman, Zhe Jiang, Xiaokang Chen, Rui Li, Yuxiang Zhang, and Kunna Li
Atmos. Chem. Phys., 22, 14059–14074, https://doi.org/10.5194/acp-22-14059-2022, https://doi.org/10.5194/acp-22-14059-2022, 2022
Short summary
Short summary
We use a deep-learning (DL) model to estimate Chinese NOx emissions by combining satellite analysis and in situ measurements. Our results are consistent with conventional analyses of Chinese NOx emissions. Comparison with mobility data shows that the DL model has a better capability to capture changes in NOx. We analyse Chinese NOx emissions during the COVID-19 pandemic lockdown period. Our results illustrate the potential use of DL as a complementary tool for conventional air quality studies.
Brendan Byrne, Junjie Liu, Yonghong Yi, Abhishek Chatterjee, Sourish Basu, Rui Cheng, Russell Doughty, Frédéric Chevallier, Kevin W. Bowman, Nicholas C. Parazoo, David Crisp, Xing Li, Jingfeng Xiao, Stephen Sitch, Bertrand Guenet, Feng Deng, Matthew S. Johnson, Sajeev Philip, Patrick C. McGuire, and Charles E. Miller
Biogeosciences, 19, 4779–4799, https://doi.org/10.5194/bg-19-4779-2022, https://doi.org/10.5194/bg-19-4779-2022, 2022
Short summary
Short summary
Plants draw CO2 from the atmosphere during the growing season, while respiration releases CO2 to the atmosphere throughout the year, driving seasonal variations in atmospheric CO2 that can be observed by satellites, such as the Orbiting Carbon Observatory 2 (OCO-2). Using OCO-2 XCO2 data and space-based constraints on plant growth, we show that permafrost-rich northeast Eurasia has a strong seasonal release of CO2 during the autumn, hinting at an unexpectedly large respiration signal from soils.
Helen M. Worden, Gene L. Francis, Susan S. Kulawik, Kevin W. Bowman, Karen Cady-Pereira, Dejian Fu, Jennifer D. Hegarty, Valentin Kantchev, Ming Luo, Vivienne H. Payne, John R. Worden, Róisín Commane, and Kathryn McKain
Atmos. Meas. Tech., 15, 5383–5398, https://doi.org/10.5194/amt-15-5383-2022, https://doi.org/10.5194/amt-15-5383-2022, 2022
Short summary
Short summary
Satellite observations of global carbon monoxide (CO) are essential for understanding atmospheric chemistry and pollution sources. This paper describes a new data product using radiance measurements from the Cross-track Infrared Sounder (CrIS) instrument on the Suomi National Polar-orbiting Partnership (SNPP) satellite that provides vertical profiles of CO from single-field-of-view observations. We show how these satellite CO profiles compare to aircraft observations and evaluate their biases.
Min Huang, James H. Crawford, Gregory R. Carmichael, Kevin W. Bowman, Sujay V. Kumar, and Colm Sweeney
Atmos. Chem. Phys., 22, 7461–7487, https://doi.org/10.5194/acp-22-7461-2022, https://doi.org/10.5194/acp-22-7461-2022, 2022
Short summary
Short summary
This study demonstrates that ozone dry-deposition modeling can be improved by revising the model's dry-deposition parameterizations to better represent the effects of environmental conditions including the soil moisture fields. Applying satellite soil moisture data assimilation is shown to also have added value. Such advancements in coupled modeling and data assimilation can benefit the assessments of ozone impacts on human and vegetation health.
Vivienne H. Payne, Susan S. Kulawik, Emily V. Fischer, Jared F. Brewer, L. Gregory Huey, Kazuyuki Miyazaki, John R. Worden, Kevin W. Bowman, Eric J. Hintsa, Fred Moore, James W. Elkins, and Julieta Juncosa Calahorrano
Atmos. Meas. Tech., 15, 3497–3511, https://doi.org/10.5194/amt-15-3497-2022, https://doi.org/10.5194/amt-15-3497-2022, 2022
Short summary
Short summary
We compare new satellite measurements of peroxyacetyl nitrate (PAN) with reference aircraft measurements from two different instruments flown on the same platform. While there is a systematic difference between the two aircraft datasets, both show the same large-scale distribution of PAN and the discrepancy between aircraft datasets is small compared to the satellite uncertainties. The satellite measurements show skill in capturing large-scale variations in PAN.
Takashi Sekiya, Kazuyuki Miyazaki, Henk Eskes, Kengo Sudo, Masayuki Takigawa, and Yugo Kanaya
Atmos. Meas. Tech., 15, 1703–1728, https://doi.org/10.5194/amt-15-1703-2022, https://doi.org/10.5194/amt-15-1703-2022, 2022
Short summary
Short summary
This study gives a systematic comparison of TROPOMI version 1.2 and OMI QA4ECV tropospheric NO2 column through global chemical data assimilation (DA) integration for April–May 2018. DA performance is controlled by measurement sensitivities, retrieval errors, and coverage. Due to reduced errors in TROPOMI, agreements against assimilated and independent observations were improved by TROPOMI DA compared to OMI DA. These results demonstrate that TROPOMI DA improves global analyses of NO2 and ozone.
Min Huang, James H. Crawford, Joshua P. DiGangi, Gregory R. Carmichael, Kevin W. Bowman, Sujay V. Kumar, and Xiwu Zhan
Atmos. Chem. Phys., 21, 11013–11040, https://doi.org/10.5194/acp-21-11013-2021, https://doi.org/10.5194/acp-21-11013-2021, 2021
Short summary
Short summary
This study evaluates the impact of satellite soil moisture data assimilation on modeled weather and ozone fields at various altitudes above the southeastern US during the summer. It emphasizes the importance of soil moisture in the understanding of surface ozone pollution and upper tropospheric chemistry, as well as air pollutants’ source–receptor relationships between the US and its downwind areas.
Zhe Jiang, Hongrong Shi, Bin Zhao, Yu Gu, Yifang Zhu, Kazuyuki Miyazaki, Xin Lu, Yuqiang Zhang, Kevin W. Bowman, Takashi Sekiya, and Kuo-Nan Liou
Atmos. Chem. Phys., 21, 8693–8708, https://doi.org/10.5194/acp-21-8693-2021, https://doi.org/10.5194/acp-21-8693-2021, 2021
Short summary
Short summary
We use the COVID-19 pandemic as a unique natural experiment to obtain a more robust understanding of the effectiveness of emission reductions toward air quality improvement by combining chemical transport simulations and observations. Our findings imply a shift from current control policies in California: a strengthened control on primary PM2.5 emissions and a well-balanced control on NOx and volatile organic compounds are needed to effectively and sustainably alleviate PM2.5 and O3 pollution.
Cited articles
Altmann, A., Toloşi, L., Sander, O., and Lengauer, T.: Permutation importance: a corrected feature importance measure, Bioinformatics, 26, 1340–1347, 2010. a
Archibald, A. T., Neu, J. L., Elshorbany, Y. F., Cooper, O. R., Young, P. J., Akiyoshi, H., Cox, R. A., Coyle, M., Derwent, R. G., Deushi, M., Finco, A., Frost, G. J., Galbally, I. E., Gerosa, G., Granier, C., Griffiths, P. T., Hossaini, R., Hu, L., Jöckel, P., Josse, B., Lin, M. Y., Mertens, M., Morgenstern, O., Naja, M., Naik, V., Oltmans, S., Plummer, D. A., Revell, L. E., Saiz-Lopez, A., Saxena, P., Shin, Y. M., Shahid, I., Shallcross, D., Tilmes, S., Trickl, T., Wallington, T. J., Wang, T., Worden, H. M., and Zeng, G.: Tropospheric Ozone Assessment Report: A critical review of changes in the tropospheric ozone burden and budget from 1850 to 2100, Elementa: Science of the Anthropocene, 8, 034, https://doi.org/10.1525/elementa.2020.034, 2020. a
Bauwens, M., Compernolle, S., Stavrakou, T., Müller, J.-F., van Gent, J., Eskes, H., Levelt, P. F., van der A, R., Veefkind, J. P., Vlietinck, J., Yu, H., and Zehner, C.: Impact of Coronavirus outbreak on NO2 pollution assessed using TROPOMI and OMI observations, Geophys. Res. Lett., 47, e2020GL087978, https://doi.org/10.1029/2020GL087978, 2020. a
Betancourt, C., Stomberg, T. T., Edrich, A.-K., Patnala, A., Schultz, M. G., Roscher, R., Kowalski, J., and Stadtler, S.: Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties, Geosci. Model Dev., 15, 4331–4354, https://doi.org/10.5194/gmd-15-4331-2022, 2022. a, b
Boersma, K., Eskes, H., Richter, A., De Smedt, I., Lorente, A., Beirle, S., Van Geffen, J., Peters, E., Van Roozendael, M., and Wagner, T.: QA4ECV NO2 tropospheric and stratospheric vertical column data from OMI (Version 1.1), Royal Netherlands Meteorological Institute (KNMI) [data set], https://doi.org/10.21944/qa4ecv-no2-omi-v1.1, 2017. a
Boersma, K. F., Eskes, H. J., Richter, A., De Smedt, I., Lorente, A., Beirle, S., van Geffen, J. H. G. M., Zara, M., Peters, E., Van Roozendael, M., Wagner, T., Maasakkers, J. D., van der A, R. J., Nightingale, J., De Rudder, A., Irie, H., Pinardi, G., Lambert, J.-C., and Compernolle, S. C.: Improving algorithms and uncertainty estimates for satellite NO2 retrievals: results from the quality assurance for the essential climate variables (QA4ECV) project, Atmos. Meas. Tech., 11, 6651–6678, https://doi.org/10.5194/amt-11-6651-2018, 2018. a
Bowman, K. W.: Toward the next generation of air quality monitoring: ozone, Atmos. Environ., 80, 571–583, 2013. a
Bowman, K. W., Rodgers, C. D., Kulawik, S. S., Worden, J., Sarkissian, E., Osterman, G., Steck, T., Ming, L., Eldering, A., Shephard, M., Worden, H., Lampel, M., Clough, S., Brown, P., Rinsland, C., Gunson, M., and Beer, R.: Tropospheric emission spectrometer: retrieval method and error analysis, IEEE T. Geosci. Remote, 44, 1297–1307, 2006. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a
Brewer, J. F., Millet, D. B., Wells, K. C., Payne, V. H., Kulawik, S., Vigouroux, C., Cady-Pereira, K. E., Pernak, R., and Zhou, M.: Space-based observations of tropospheric ethane map emissions from fossil fuel extraction, Nat. Commun., 15, 7829, https://doi.org/10.1038/s41467-024-52247-z, 2024. a
Cao, H., Henze, D. K., Zhu, L., Shephard, M. W., Cady-Pereira, K., Dammers, E., Sitwell, M., Heath, N., Lonsdale, C., Bash, J. O., Miyazaki, K., Flechard, C., Fauvel, Y., Kruit, R. W., Feigenspan, S., Brümmer, C., Schrader, F., Twigg, M. M., Leeson, S., Tang, Y. S., Stephens, A. C. M., Braban, C., Vincent, K., Meier, M., Seitler, E., Geels, C., Ellermann, T., Sanocka, A., and Capps, S. L.: 4D-Var inversion of European NH3 emissions using CrIS NH3 measurements and GEOS-Chem adjoint with bi-directional and uni-directional flux schemes, J. Geophys. Res.-Atmos., 127, e2021JD035687, https://doi.org/10.1029/2021JD035687, 2022. a
Chen, G., Chen, J., hui Dong, G., yi Yang, B., Liu, Y., Lu, T., Yu, P., Guo, Y., and Li, S.: Improving satellite-based estimation of surface ozone across China during 2008–2019 using iterative random forest model and high-resolution grid meteorological data, Sustain. Cities Soc., 69, 102807, https://doi.org/10.1016/j.scs.2021.102807, 2021. a
Clerbaux, C., Boynard, A., Clarisse, L., George, M., Hadji-Lazaro, J., Herbin, H., Hurtmans, D., Pommier, M., Razavi, A., Turquety, S., Wespes, C., and Coheur, P.-F.: Monitoring of atmospheric composition using the thermal infrared IASI/MetOp sounder, Atmos. Chem. Phys., 9, 6041–6054, https://doi.org/10.5194/acp-9-6041-2009, 2009. a, b
Colombi, N., Miyazaki, K., Bowman, K. W., Neu, J. L., and Jacob, D. J.: A new methodology for inferring surface ozone from multispectral satellite measurements, Environ. Res. Lett., 16, 105005, https://doi.org/10.1088/1748-9326/ac243d, 2021. a, b
Cooper, O. R., Chang, K.-L., Bates, K., Brown, S. S., Chace, W. S., Coggon, M. M., Gorchov Negron, A. M., Middlebrook, A. M., Peischl, J., Piasecki, A., Schafer, N., Stockwell, C. E., Wang, S., Warneke, C., Zuraski, K., Miyazaki, K., Payne, V. H., Pennington, E. A., Worden, J. R., Bowman, K. W., and McDonald, B. C.: Early season 2023 wildfires generated record-breaking surface ozone anomalies across the U.S. Upper Midwest, Geophys. Res. Lett., 51, e2024GL111481, https://doi.org/10.1029/2024GL111481, 2024. a
De Smedt, I., Müller, J.-F., Stavrakou, T., van der A, R., Eskes, H., and Van Roozendael, M.: Twelve years of global observations of formaldehyde in the troposphere using GOME and SCIAMACHY sensors, Atmos. Chem. Phys., 8, 4947–4963, https://doi.org/10.5194/acp-8-4947-2008, 2008. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Koehler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, 2011. a
Deeter, M. N., Edwards, D. P., Francis, G. L., Gille, J. C., Martínez-Alonso, S., Worden, H. M., and Sweeney, C.: A climate-scale satellite record for carbon monoxide: the MOPITT Version 7 product, Atmos. Meas. Tech., 10, 2533–2555, https://doi.org/10.5194/amt-10-2533-2017, 2017. a, b
Elshorbany, Y., Ziemke, J. R., Strode, S., Petetin, H., Miyazaki, K., De Smedt, I., Pickering, K., Seguel, R. J., Worden, H., Emmerichs, T., Taraborrelli, D., Cazorla, M., Fadnavis, S., Buchholz, R. R., Gaubert, B., Rojas, N. Y., Nogueira, T., Salameh, T., and Huang, M.: Tropospheric ozone precursors: global and regional distributions, trends, and variability, Atmos. Chem. Phys., 24, 12225–12257, https://doi.org/10.5194/acp-24-12225-2024, 2024. a, b
Fleming, Z. L., Doherty, R. M., von Schneidemesser, E., Malley, C. S., Cooper, O. R., Pinto, J. P., Colette, A., Xu, X., Simpson, D., Schultz, M. G., Lefohn, A. S., Hamad, S., Moolla, R., Solberg, S., and Feng, Z.: Tropospheric Ozone Assessment Report: Present-day ozone distribution and trends relevant to human health, Elementa: Science of the Anthropocene, 6, 12, https://doi.org/10.1525/elementa.273, 2018. a
Fu, D., Kulawik, S. S., Miyazaki, K., Bowman, K. W., Worden, J. R., Eldering, A., Livesey, N. J., Teixeira, J., Irion, F. W., Herman, R. L., Osterman, G. B., Liu, X., Levelt, P. F., Thompson, A. M., and Luo, M.: Retrievals of tropospheric ozone profiles from the synergism of AIRS and OMI: methodology and validation, Atmos. Meas. Tech., 11, 5587–5605, https://doi.org/10.5194/amt-11-5587-2018, 2018. a, b
Gaudel, A., Cooper, O. R., Ancellet, G., Barret, B., Boynard, A., Burrows, J. P., Clerbaux, C., Coheur, P.-F., Cuesta, J., Cuevas, E., Doniki, S., Dufour, G., Ebojie, F., Foret, G., Garcia, O., Granados-Muñoz, M. J., Hannigan, J. W., Hase, F., Hassler, B., Huang, G., Hurtmans, D., Jaffe, D., Jones, N., Kalabokas, P., Kerridge, B., Kulawik, S., Latter, B., Leblanc, T., Le Flochmoën, E., Lin, W., Liu, J., Liu, X., Mahieu, E., McClure-Begley, A., Neu, J. L., Osman, M., Palm, M., Petetin, H., Petropavlovskikh, I., Querel, R., Rahpoe, N., Rozanov, A., Schultz, M. G., Schwab, J., Siddans, R., Smale, D., Steinbacher, M., Tanimoto, H., Tarasick, D. W., Thouret, V., Thompson, A. M., Trickl, T., Weatherhead, E., Wespes, C., Worden, H. M., Vigouroux, C., Xu, X., Zeng, G., and Ziemke, J.: Tropospheric Ozone Assessment Report: Present-day distribution and trends of tropospheric ozone relevant to climate and global atmospheric chemistry model evaluation, Elementa: Science of the Anthropocene, 6, 39, https://doi.org/10.1525/elementa.291, 2018. a
Geer, A. J.: Learning earth system models from observations: machine learning or data assimilation?, Philos. T. Roy. Soc. A, 379, 20200089, https://doi.org/10.1098/rsta.2020.0089, 2021. a
Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492, https://doi.org/10.5194/gmd-5-1471-2012, 2012. a
He, T.-L., Jones, D. B. A., Miyazaki, K., Bowman, K. W., Jiang, Z., Chen, X., Li, R., Zhang, Y., and Li, K.: Inverse modelling of Chinese NOx emissions using deep learning: integrating in situ observations with a satellite-based chemical reanalysis, Atmos. Chem. Phys., 22, 14059–14074, https://doi.org/10.5194/acp-22-14059-2022, 2022a. a
He, T.-L., Jones, D. B. A., Miyazaki, K., Huang, B., Liu, Y., Jiang, Z., White, E. C., Worden, H. M., and Worden, J. R.: Deep learning to evaluate US NOx emissions using surface ozone predictions, J. Geophys. Res.-Atmos., 127, e2021JD035597, https://doi.org/10.1029/2021JD035597, 2022b. a
Health Effects Institute: State of Global Air 2024. Special Report, Boston, MA, Health Effects Institute, https://www.stateofglobalair.org/resources/report/state-global-air-report-2024 (last access: 1 November 2024), 2024. a
Hickman, S. H. M., Kelp, M., Griffiths, P. T., Doerksen, K., Miyazaki, K., Pennington, E. A., Koren, G., Iglesias-Suarez, F., Schultz, M. G., Chang, K.-L., Cooper, O. R., Archibald, A. T., Sommariva, R., Carlson, D., Wang, H., West, J. J., and Liu, Z.: Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-3739, 2025. a
Huijnen, V., Miyazaki, K., Flemming, J., Inness, A., Sekiya, T., and Schultz, M. G.: An intercomparison of tropospheric ozone reanalysis products from CAMS, CAMS interim, TCR-1, and TCR-2, Geosci. Model Dev., 13, 1513–1544, https://doi.org/10.5194/gmd-13-1513-2020, 2020. a
Hunt, B. R., Kostelich, E. J., and Szunyogh, I.: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter, Physica D, 230, 112–126, https://doi.org/10.1016/j.physd.2006.11.008, 2007. a
Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019. a, b, c
Ivatt, P. D. and Evans, M. J.: Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees, Atmos. Chem. Phys., 20, 8063–8082, https://doi.org/10.5194/acp-20-8063-2020, 2020. a
Jaffe, D. A. and Wigder, N. L.: Ozone production from wildfires: a critical review, Atmos. Environ., 51, 1–10, https://doi.org/10.1016/j.atmosenv.2011.11.063, 2012. a
Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Dentener, F., Muntean, M., Pouliot, G., Keating, T., Zhang, Q., Kurokawa, J., Wankmüller, R., Denier van der Gon, H., Kuenen, J. J. P., Klimont, Z., Frost, G., Darras, S., Koffi, B., and Li, M.: HTAP_v2.2: a mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution, Atmos. Chem. Phys., 15, 11411–11432, https://doi.org/10.5194/acp-15-11411-2015, 2015. a
Jin, X., Fiore, A. M., and Cohen, R. C.: Space-based observations of ozone precursors within California wildfire plumes and the impacts on ozone-NOx-VOC chemistry, Environ. Sci. Technol., 57, 14648–14660, https://doi.org/10.1021/acs.est.3c04411, 2023. a
Jones, D., Prates, L., Qu, Z., Cheng, W., Miyazaki, K., Sekiya, T., Inness, A., Kumar, R., Tang, X., Worden, H., Koren, G., and Huijen, V.: Assessment of regional and interannual variations in tropospheric ozone in chemical reanalyses, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-3759, 2025. a, b
Kanaya, Y., Miyazaki, K., Taketani, F., Miyakawa, T., Takashima, H., Komazaki, Y., Pan, X., Kato, S., Sudo, K., Sekiya, T., Inoue, J., Sato, K., and Oshima, K.: Ozone and carbon monoxide observations over open oceans on R/V Mirai from 67° S to 75° N during 2012 to 2017: testing global chemical reanalysis in terms of Arctic processes, low ozone levels at low latitudes, and pollution transport, Atmos. Chem. Phys., 19, 7233–7254, https://doi.org/10.5194/acp-19-7233-2019, 2019. a
Keller, C. A. and Evans, M. J.: Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10, Geosci. Model Dev., 12, 1209–1225, https://doi.org/10.5194/gmd-12-1209-2019, 2019. a
Keller, C. A., Evans, M. J., Knowland, K. E., Hasenkopf, C. A., Modekurty, S., Lucchesi, R. A., Oda, T., Franca, B. B., Mandarino, F. C., Díaz Suárez, M. V., Ryan, R. G., Fakes, L. H., and Pawson, S.: Global impact of COVID-19 restrictions on the surface concentrations of nitrogen dioxide and ozone, Atmos. Chem. Phys., 21, 3555–3592, https://doi.org/10.5194/acp-21-3555-2021, 2021. a
Kelp, M. M., Jacob, D. J., Lin, H., and Sulprizio, M. P.: An online-learned neural network chemical solver for stable long-term global simulations of atmospheric chemistry, J. Adv. Model. Earth Sy., 14, e2021MS002926, https://doi.org/10.1029/2021MS002926, 2022. a
Kleinert, F., Leufen, L. H., Lupascu, A., Butler, T., and Schultz, M. G.: Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework, Geosci. Model Dev., 15, 8913–8930, https://doi.org/10.5194/gmd-15-8913-2022, 2022. a
Ko, K., Cho, S., and Rao, R. R.: Machine-learning-based near-surface ozone forecasting model with planetary boundary layer information, Sensors, 22, 7864, https://doi.org/10.3390/s22207864, 2022. a
Krotkov, N. A., McLinden, C. A., Li, C., Lamsal, L. N., Celarier, E. A., Marchenko, S. V., Swartz, W. H., Bucsela, E. J., Joiner, J., Duncan, B. N., Boersma, K. F., Veefkind, J. P., Levelt, P. F., Fioletov, V. E., Dickerson, R. R., He, H., Lu, Z., and Streets, D. G.: Aura OMI observations of regional SO2 and NO2 pollution changes from 2005 to 2015, Atmos. Chem. Phys., 16, 4605–4629, https://doi.org/10.5194/acp-16-4605-2016, 2016. a
Kuz'min, V. E., Polishchuk, P. G., Artemenko, A. G., and Andronati, S. A.: Interpretation of QSAR models based on random forest methods, Mol. Inform., 30, 593–603, 2011. a
Lacima, A., Petetin, H., Soret, A., Bowdalo, D., Jorba, O., Chen, Z., Méndez Turrubiates, R. F., Achebak, H., Ballester, J., and Pérez García-Pando, C.: Long-term evaluation of surface air pollution in CAMSRA and MERRA-2 global reanalyses over Europe (2003–2020), Geosci. Model Dev., 16, 2689–2718, https://doi.org/10.5194/gmd-16-2689-2023, 2023. a
Lahoz W. A. and Schneider P.: Data assimilation: making sense of Earth Observation, Front. Environ. Sci., 2, 16, https://doi.org/10.3389/fenvs.2014.00016, 2014. a
Levelt, P. F., Joiner, J., Tamminen, J., Veefkind, J. P., Bhartia, P. K., Stein Zweers, D. C., Duncan, B. N., Streets, D. G., Eskes, H., van der A, R., McLinden, C., Fioletov, V., Carn, S., de Laat, J., DeLand, M., Marchenko, S., McPeters, R., Ziemke, J., Fu, D., Liu, X., Pickering, K., Apituley, A., González Abad, G., Arola, A., Boersma, F., Chan Miller, C., Chance, K., de Graaf, M., Hakkarainen, J., Hassinen, S., Ialongo, I., Kleipool, Q., Krotkov, N., Li, C., Lamsal, L., Newman, P., Nowlan, C., Suleiman, R., Tilstra, L. G., Torres, O., Wang, H., and Wargan, K.: The Ozone Monitoring Instrument: overview of 14 years in space, Atmos. Chem. Phys., 18, 5699–5745, https://doi.org/10.5194/acp-18-5699-2018, 2018. a
Li, S., Wang, T., Huang, X., Pu, X., Li, M., Chen, P., Yang, X.-Q., and Wang, M.: Impact of East Asian summer monsoon on surface ozone pattern in China, J. Geophys. Res.-Atmos., 123, 1401–1411, https://doi.org/10.1002/2017JD027190, 2018. a
Liu, Z., Doherty, R. M., Wild, O., O'Connor, F. M., and Turnock, S. T.: Correcting ozone biases in a global chemistry–climate model: implications for future ozone, Atmos. Chem. Phys., 22, 12543–12557, https://doi.org/10.5194/acp-22-12543-2022, 2022. a, b, c, d
Livesey, N. J., Read, W. G., Wagner, P. A., Froidevaux, L., Lambert, A., Manney, G. L., Millán Valle, L. F., Pumphrey, H. C., Santee, M. L., Schwartz, M. J., Wang, S., Fuller, R. A., Jarnot, R. F., Knosp, B. W., Martinez, E., and Lay, R. R.: Version 4.2x Level 2 data quality and description document, Jet Propul, Tech. rep., Lab., Tech. Rep. JPL D-33509 Rev. D, Pasadena, CA, USA, https://mls.jpl.nasa.gov/data/v4-2_data_quality_document.pdf (last access: 1 November 2024), 2018. a
Lloyd, S.: Least squares quantization in PCM, IEEE Transactions on Information Theory, 28, 129–137, https://doi.org/10.1109/TIT.1982.1056489, 1982. a
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I.: From local explanations to global understanding with explainable AI for trees, Nature Machine Intelligence, 2, 56–67, https://doi.org/10.1038/s42256-019-0138-9, 2020. a, b
McGovern, A., Lagerquist, R., Gagne, D. J., Jergensen, G. E., Elmore, K. L., Homeyer, C. R., and Smith, T.: Making the black box more transparent: understanding the physical implications of machine learning, B. Am. Meteorol. Soc., 100, 2175–2199, https://doi.org/10.1175/BAMS-D-18-0195.1, 2019. a
Meinshausen, N. and Ridgeway, G.: Quantile regression forests, J. Mach. Learn. Res., 7, 983–999, 2006. a
Mills, G., Pleijel, H., Malley, C. S., Sinha, B., Cooper, O. R., Schultz, M. G., Neufeld, H. S., Simpson, D., Sharps, K., Feng, Z., Gerosa, G., Harmens, H., Kobayashi, K., Saxena, P., Paoletti, E., Sinha, V., and Xu, X.: Tropospheric Ozone Assessment Report: Present-day tropospheric ozone distribution and trends relevant to vegetation, Elementa: Science of the Anthropocene, 6, 47, https://doi.org/10.1525/elementa.302, 2018. a
Miyazaki, K.: TROPESS Chemical Reanalysis Surface O3 2-Hourly 2-dimensional Product V1, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/NN87W53OVGUS, 2024. a
Miyazaki, K. and Bowman, K.: Predictability of fossil fuel CO2 from air quality emissions, Nat. Commun., 14, 1604, https://doi.org/10.1038/s41467-023-37264-8, 2023. a
Miyazaki, K., Eskes, H. J., Sudo, K., Takigawa, M., van Weele, M., and Boersma, K. F.: Simultaneous assimilation of satellite NO2, O3, CO, and HNO3 data for the analysis of tropospheric chemical composition and emissions, Atmos. Chem. Phys., 12, 9545–9579, https://doi.org/10.5194/acp-12-9545-2012, 2012. a
Miyazaki, K., Eskes, H. J., Sudo, K., and Zhang, C.: Global lightning NOx production estimated by an assimilation of multiple satellite data sets, Atmos. Chem. Phys., 14, 3277–3305, https://doi.org/10.5194/acp-14-3277-2014, 2014. a
Miyazaki, K., Eskes, H. J., and Sudo, K.: A tropospheric chemistry reanalysis for the years 2005–2012 based on an assimilation of OMI, MLS, TES, and MOPITT satellite data, Atmos. Chem. Phys., 15, 8315–8348, https://doi.org/10.5194/acp-15-8315-2015, 2015. a
Miyazaki, K., Eskes, H., Sudo, K., Boersma, K. F., Bowman, K., and Kanaya, Y.: Decadal changes in global surface NOx emissions from multi-constituent satellite data assimilation, Atmos. Chem. Phys., 17, 807–837, https://doi.org/10.5194/acp-17-807-2017, 2017. a, b
Miyazaki, K., Sekiya, T., Fu, D., Bowman, K., Kulawik, S., Sudo, K., Walker, T., Kanaya, Y., Takigawa, M., Ogochi, K., Eskes, H., Boersma, K. F., Thompson, A. M., Gaubert, B., Barre, J., and Emmons, L. K.: Balance of emission and dynamical controls on ozone during the Korea-United States Air Quality campaign from multiconstituent satellite data assimilation, J. Geophys. Res.-Atmos., 124, 387–413, 2019. a, b, c, d, e
Miyazaki, K., Bowman, K., Sekiya, T., Eskes, H., Boersma, F., Worden, H., Livesey, N., Payne, V. H., Sudo, K., Kanaya, Y., Takigawa, M., and Ogochi, K.: Updated tropospheric chemistry reanalysis and emission estimates, TCR-2, for 2005–2018, Earth Syst. Sci. Data, 12, 2223–2259, https://doi.org/10.5194/essd-12-2223-2020, 2020a. a, b, c, d, e
Miyazaki, K., Bowman, K. W., Yumimoto, K., Walker, T., and Sudo, K.: Evaluation of a multi-model, multi-constituent assimilation framework for tropospheric chemical reanalysis, Atmos. Chem. Phys., 20, 931–967, https://doi.org/10.5194/acp-20-931-2020, 2020b. a, b
Miyazaki, K., Bowman, K., Sekiya, T., Takigawa, M., Neu, J. L., Sudo, K., Osterman, G., and Eskes, H.: Global tropospheric ozone responses to reduced NOx emissions linked to the COVID-19 worldwide lockdowns, Science Advances, 7, eabf7460, https://doi.org/10.1126/sciadv.abf7460, 2021. a, b, c
Montgomery, J., Lu, S., and Marchetti, Y.: SUDSAQ, https://github.com/JPLMLIA/SUDSAQ, last access: 16 July 2024. a
Okamoto, S., Cuesta, J., Beekmann, M., Dufour, G., Eremenko, M., Miyazaki, K., Boonne, C., Tanimoto, H., and Akimoto, H.: Impact of different sources of precursors on an ozone pollution outbreak over Europe analysed with IASI+GOME2 multispectral satellite observations and model simulations, Atmos. Chem. Phys., 23, 7399–7423, https://doi.org/10.5194/acp-23-7399-2023, 2023. a, b
Pai, S. J., Heald, C. L., and Murphy, J. G.: Exploring the global importance of atmospheric ammonia oxidation, ACS Earth and Space Chemistry, 5, 1674–1685, https://doi.org/10.1021/acsearthspacechem.1c00021, 2021. a
Pennington, E. A., Osterman, G. B., Payne, V. H., Miyazaki, K., Bowman, K. W., and Neu, J. L.: Quantifying biases in TROPESS AIRS, CrIS, and joint AIRS+OMI tropospheric ozone products using ozonesondes, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-3701, 2024. a
Permar, W., Wang, Q., Selimovic, V., Wielgasz, C., Yokelson, R. J., Hornbrook, R. S., Hills, A. J., Apel, E. C., Ku, I.-T., Zhou, Y., Sive, B. C., Sullivan, A. P., Collett Jr., J. L., Campos, T. L., Palm, B. B., Peng, Q., Thornton, J. A., Garofalo, L. A., Farmer, D. K., Kreidenweis, S. M., Levin, E. J. T., DeMott, P. J., Flocke, F., Fischer, E. V., and Hu, L.: Emissions of trace organic gases from Western U.S. wildfires based on WE-CAN aircraft measurements, J. Geophys. Res.-Atmos., 126, e2020JD033838, https://doi.org/10.1029/2020JD033838, 2021. a
Saabas, A.: treeinterpreter, https://github.com/andosa/treeinterpreter (last access: 1 November 2024), 2015. a
Schultz, M. G., Schröder, S., Lyapina, O., Cooper, O. R., Galbally, I., Petropavlovskikh, I., von Schneidemesser, E., Tanimoto, H., Elshorbany, Y., Naja, M., Seguel, R. J., Dauert, U., Eckhardt, P., Feigenspan, S., Fiebig, M., Hjellbrekke, A.-G., Hong, Y.-D., Kjeld, P. C., Koide, H., Lear, G., Tarasick, D., Ueno, M., Wallasch, M., Baumgardner, D., Chuang, M.-T., Gillett, R., Lee, M., Molloy, S., Moolla, R., Wang, T., Sharps, K., Adame, J. A., Ancellet, G., Apadula, F., Artaxo, P., Barlasina, M. E., Bogucka, M., Bonasoni, P., Chang, L., Colomb, A., Cuevas-Agulló, E., Cupeiro, M., Degorska, A., Ding, A., Fröhlich, M., Frolova, M., Gadhavi, H., Gheusi, F., Gilge, S., Gonzalez, M. Y., Gros, V., Hamad, S. H., Helmig, D., Henriques, D., Hermansen, O., Holla, R., Hueber, J., Im, U., Jaffe, D. A., Komala, N., Kubistin, D., Lam, K.-S., Laurila, T., Lee, H., Levy, I., Mazzoleni, C., Mazzoleni, L. R., McClure-Begley, A., Mohamad, M., Murovec, M., Navarro-Comas, M., Nicodim, F., Parrish, D., Read, K. A., Reid, N., Ries, L., Saxena, P., Schwab, J. J., Scorgie, Y., Senik, I., Simmonds, P., Sinha, V., Skorokhod, A. I., Spain, G., Spangl, W., Spoor, R., Springston, S. R., Steer, K., Steinbacher, M., Suharguniyawan, E., Torre, P., Trickl, T., Weili, L., Weller, R., Xiaobin, X., Xue, L., and Zhiqiang, M.: Tropospheric Ozone Assessment Report: Database and metrics data of global surface ozone observations, Elementa: Science of the Anthropocene, 5, 58, https://doi.org/10.1525/elementa.244, 2017. a, b
Sculley, D.: Web-scale k-means clustering, in: The 19th international conference on World wide web, 26–30 April 2011, Raleigh, NC, USA, 1177–1178, https://doi.org/10.1145/1772690.1772862, 2011. a
Sekiya, T., Miyazaki, K., Ogochi, K., Sudo, K., Takigawa, M., Eskes, H., and Boersma, K. F.: Impacts of horizontal resolution on global data assimilation of satellite measurements for tropospheric chemistry analysis, J. Adv. Model. Earth Sy., 13, e2020MS002180, https://doi.org/10.1029/2020MS002180, 2021. a
Sekiya, T., Miyazaki, K., Eskes, H., Bowman, K., Sudo, K., Kanaya, Y., and Takigawa, M.: The worldwide COVID-19 lockdown impacts on global secondary inorganic aerosols and radiative budget, Science Advances, 9, eadh2688, https://doi.org/10.1126/sciadv.adh2688, 2023. a
Sekiya, T., Emili, E., Miyazaki, K., Inness, A., Qu, Z., Pierce, R. B., Jones, D., Worden, H., Cheng, W. Y. Y., Huijnen, V., and Koren, G.: Assessing the relative impacts of satellite ozone and its precursor observations to improve global tropospheric ozone analysis using multiple chemical reanalysis systems, Atmos. Chem. Phys., 25, 2243–2268, https://doi.org/10.5194/acp-25-2243-2025, 2025. a, b, c
Shogrin, M. J., Payne, V. H., Kulawik, S. S., Miyazaki, K., and Fischer, E. V.: Measurement report: Spatiotemporal variability of peroxy acyl nitrates (PANs) over Mexico City from TES and CrIS satellite measurements, Atmos. Chem. Phys., 23, 2667–2682, https://doi.org/10.5194/acp-23-2667-2023, 2023. a
Sillman, S.: The relation between ozone, NOx and hydrocarbons in urban and polluted rural environments, Atmos. Environ., 33, 1821–1845, https://doi.org/10.1016/S1352-2310(98)00345-8, 1999. a
Silva, S. J. and Keller, C. A.: Limitations of XAI methods for process-level understanding in the atmospheric sciences, Artificial Intelligence for the Earth Systems, 3, e230045, https://doi.org/10.1175/AIES-D-23-0045.1, 2024. a
Skipper, T. N., Hogrefe, C., Henderson, B. H., Mathur, R., Foley, K. M., and Russell, A. G.: Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ, Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, 2024. a, b
Souri, A. H., González Abad, G., Wolfe, G. M., Verhoelst, T., Vigouroux, C., Pinardi, G., Compernolle, S., Langerock, B., Duncan, B. N., and Johnson, M. S.: Feasibility of robust estimates of ozone production rates using a synergy of satellite observations, ground-based remote sensing, and models, Atmos. Chem. Phys., 25, 2061–2086, https://doi.org/10.5194/acp-25-2061-2025, 2025. a
Sun, Z., Sandoval, L., Crystal-Ornelas, R., Mousavi, S. M., Wang, J., Lin, C., Cristea, N., Tong, D., Carande, W. H., Ma, X., Rao, Y., Bednar, J. A., Tan, A., Wang, J., Purushotham, S., Gill, T. E., Chastang, J., Howard, D., Holt, B., Gangodagamage, C., Zhao, P., Rivas, P., Chester, Z., Orduz, J., and John, A.: A review of Earth artificial intelligence, Comput. Geosci., 159, 105034, https://doi.org/10.1016/j.cageo.2022.105034, 2022. a
Travis, K. R., Jacob, D. J., Fisher, J. A., Kim, P. S., Marais, E. A., Zhu, L., Yu, K., Miller, C. C., Yantosca, R. M., Sulprizio, M. P., Thompson, A. M., Wennberg, P. O., Crounse, J. D., St. Clair, J. M., Cohen, R. C., Laughner, J. L., Dibb, J. E., Hall, S. R., Ullmann, K., Wolfe, G. M., Pollack, I. B., Peischl, J., Neuman, J. A., and Zhou, X.: Why do models overestimate surface ozone in the Southeast United States?, Atmos. Chem. Phys., 16, 13561–13577, https://doi.org/10.5194/acp-16-13561-2016, 2016. a
Veefkind, J., Aben, I., McMullan, K., Förster, H., de Vries, J., Otter, G., Claas, J., Eskes, H., de Haan, J., Kleipool, Q., van Weele, M., Hasekamp, O., Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingmann, P., Voors, R., Kruizinga, B., Vink, R., Visser, H., and Levelt, P.: TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications, Remote Sens. Environ., 120, 70–83, https://doi.org/10.1016/j.rse.2011.09.027, 2012. a
Wang, H., Miyazaki, K., Sun, H. Z., Qu, Z., Liu, X., Inness, A., Schultz, M., Schröder, S., Serre, M., and West, J. J.: Intercomparison of global ground-level ozone datasets for health-relevant metrics, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-3723, 2025. a, b
Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E., Takata, K., Emori, S., and Kawamiya, M.: MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments, Geosci. Model Dev., 4, 845–872, https://doi.org/10.5194/gmd-4-845-2011, 2011. a
Watson, G. L., Telesca, D., Reid, C. E., Pfister, G. G., and Jerrett, M.: Machine learning models accurately predict ozone exposure during wildfire events, Environ. Pollut., 254, 112792, https://doi.org/10.1016/j.envpol.2019.06.088, 2019. a
Weng, X., Forster, G. L., and Nowack, P.: A machine learning approach to quantify meteorological drivers of ozone pollution in China from 2015 to 2019, Atmos. Chem. Phys., 22, 8385–8402, https://doi.org/10.5194/acp-22-8385-2022, 2022. a
Xiao, Y., Logan, J. A., Jacob, D. J., Hudman, R. C., Yantosca, R., and Blake, D. R.: Global budget of ethane and regional constraints on U.S. sources, J. Geophys. Res.-Atmos., 113, D21306, https://doi.org/10.1029/2007JD009415, 2008. a
Xu, L., Crounse, J. D., Vasquez, K. T., Allen, H., Wennberg, P. O., Bourgeois, I., Brown, S. S., Campuzano-Jost, P., Coggon, M. M., Crawford, J. H., DiGangi, J. P., Diskin, G. S., Fried, A., Gargulinski, E. M., Gilman, J. B., Gkatzelis, G. I., Guo, H., Hair, J. W., Hall, S. R., Halliday, H. A., Hanisco, T. F., Hannun, R. A., Holmes, C. D., Huey, L. G., Jimenez, J. L., Lamplugh, A., Lee, Y. R., Liao, J., Lindaas, J., Neuman, J. A., Nowak, J. B., Peischl, J., Peterson, D. A., Piel, F., Richter, D., Rickly, P. S., Robinson, M. A., Rollins, A. W., Ryerson, T. B., Sekimoto, K., Selimovic, V., Shingler, T., Soja, A. J., Clair, J. M. S., Tanner, D. J., Ullmann, K., Veres, P. R., Walega, J., Warneke, C., Washenfelder, R. A., Weibring, P., Wisthaler, A., Wolfe, G. M., Womack, C. C., and Yokelson, R. J.: Ozone chemistry in western U.S. wildfire plumes, Science Advances, 7, eabl3648, https://doi.org/10.1126/sciadv.abl3648, 2021. a
Young, P. J., Naik, V., Fiore, A. M., Gaudel, A., Guo, J., Lin, M. Y., Neu, J. L., Parrish, D. D., Rieder, H. E., Schnell, J. L., Tilmes, S., Wild, O., Zhang, L., Ziemke, J., Brandt, J., Delcloo, A., Doherty, R. M., Geels, C., Hegglin, M. I., Hu, L., Im, U., Kumar, R., Luhar, A., Murray, L., Plummer, D., Rodriguez, J., Saiz-Lopez, A., Schultz, M. G., Woodhouse, M. T., and Zeng, G.: Tropospheric Ozone Assessment Report: Assessment of global-scale model performance for global and regional ozone distributions, variability, and trends, Elementa: Science of the Anthropocene, 6, 10, https://doi.org/10.1525/elementa.265, 2018. a, b
Short summary
This study employs explainable machine learning to analyze the causes of significant biases in surface ozone estimates from chemical reanalysis. By analyzing global observations and chemical reanalysis outputs, key bias drivers, such as meteorological conditions and precursor emissions, were identified. This provides actionable insights to improve chemical transport models, observation systems, and emissions inventories, ultimately enhancing ozone reanalysis for better air pollution management.
This study employs explainable machine learning to analyze the causes of significant biases in...
Altmetrics
Final-revised paper
Preprint