Articles | Volume 20, issue 2
https://doi.org/10.5194/acp-20-931-2020
https://doi.org/10.5194/acp-20-931-2020
Research article
 | 
24 Jan 2020
Research article |  | 24 Jan 2020

Evaluation of a multi-model, multi-constituent assimilation framework for tropospheric chemical reanalysis

Kazuyuki Miyazaki, Kevin W. Bowman, Keiya Yumimoto, Thomas Walker, and Kengo Sudo

Related authors

Quantifying biases in TROPESS AIRS, CrIS, and joint AIRS+OMI tropospheric ozone products using ozonesondes
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
Identifying drivers of surface ozone bias in global chemical reanalysis with explainable machine learning
Kazuyuki Miyazaki, Yuliya Marchetti, James Montgomery, Steven Lu, and Kevin Bowman
Atmos. Chem. Phys., 25, 8507–8532, https://doi.org/10.5194/acp-25-8507-2025,https://doi.org/10.5194/acp-25-8507-2025, 2025
Short summary
Trace gas atmospheric rivers: remote drivers of air pollutants
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
Short summary
Assessing the relative impacts of satellite ozone and its precursor observations to improve global tropospheric ozone analysis using multiple chemical reanalysis systems
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
Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation
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

Related subject area

Subject: Gases | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
Contributions of lightning to long-term trends and inter-annual variability in global atmospheric chemistry constrained by Schumann resonance observations
Xiaobo Wang, Yuzhong Zhang, Tamás Bozóki, Ruosi Liang, Xinchun Xie, Shutao Zhao, Rui Wang, Yujia Zhao, and Shuai Sun
Atmos. Chem. Phys., 25, 8929–8942, https://doi.org/10.5194/acp-25-8929-2025,https://doi.org/10.5194/acp-25-8929-2025, 2025
Short summary
Climate-driven biogenic emissions alleviate the impact of human-made emission reductions on O3 control in the Pearl River Delta region, southern China
Nan Wang, Song Liu, Jiawei Xu, Yanyu Wang, Chun Li, Yuning Xie, Hua Lu, and Fumo Yang
Atmos. Chem. Phys., 25, 8859–8870, https://doi.org/10.5194/acp-25-8859-2025,https://doi.org/10.5194/acp-25-8859-2025, 2025
Short summary
Impacts of wildfire smoke aerosols on near-surface ozone photochemistry
Jiaqi Shen, Ronald C. Cohen, Glenn M. Wolfe, and Xiaomeng Jin
Atmos. Chem. Phys., 25, 8701–8718, https://doi.org/10.5194/acp-25-8701-2025,https://doi.org/10.5194/acp-25-8701-2025, 2025
Short summary
Natural surface emissions dominate anthropogenic emissions contributions to total gaseous mercury at Canadian rural sites
Irene Cheng, Amanda Cole, Leiming Zhang, and Alexandra Steffen
Atmos. Chem. Phys., 25, 8591–8611, https://doi.org/10.5194/acp-25-8591-2025,https://doi.org/10.5194/acp-25-8591-2025, 2025
Short summary
Modelling Arctic lower-tropospheric ozone: processes controlling seasonal variations
Wanmin Gong, Stephen R. Beagley, Kenjiro Toyota, Henrik Skov, Jesper Heile Christensen, Alex Lupu, Diane Pendlebury, Junhua Zhang, Ulas Im, Yugo Kanaya, Alfonso Saiz-Lopez, Roberto Sommariva, Peter Effertz, John W. Halfacre, Nis Jepsen, Rigel Kivi, Theodore K. Koenig, Katrin Müller, Claus Nordstrøm, Irina Petropavlovskikh, Paul B. Shepson, William R. Simpson, Sverre Solberg, Ralf M. Staebler, David W. Tarasick, Roeland Van Malderen, and Mika Vestenius
Atmos. Chem. Phys., 25, 8355–8405, https://doi.org/10.5194/acp-25-8355-2025,https://doi.org/10.5194/acp-25-8355-2025, 2025
Short summary

Cited articles

Abida, R., Attié, J.-L., El Amraoui, L., Ricaud, P., Lahoz, W., Eskes, H., Segers, A., Curier, L., de Haan, J., Kujanpää, J., Nijhuis, A. O., Tamminen, J., Timmermans, R., and Veefkind, P.: Impact of spaceborne carbon monoxide observations from the S-5P platform on tropospheric composition analyses and forecasts, Atmos. Chem. Phys., 17, 1081–1103, https://doi.org/10.5194/acp-17-1081-2017, 2017. 
Anderson, J. L.: An adaptive covariance inflation error correction algorithm for ensemble filters, Tellus, 59A, 210–224, https://doi.org/10.1111/j.1600-0870.2006.00216.x, 2007. 
Archibald, A. T., Cooke, M. C., Utembe, S. R., Shallcross, D. E., Derwent, R. G., and Jenkin, M. E.: Impacts of mechanistic changes on HOx formation and recycling in the oxidation of isoprene, Atmos. Chem. Phys., 10, 8097–8118, https://doi.org/10.5194/acp-10-8097-2010, 2010. 
Bocquet, M., Elbern, H., Eskes, H., Hirtl, M., Žabkar, R., Carmichael, G. R., Flemming, J., Inness, A., Pagowski, M., Pérez Camaño, J. L., Saide, P. E., San Jose, R., Sofiev, M., Vira, J., Baklanov, A., Carnevale, C., Grell, G., and Seigneur, C.: Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models, Atmos. Chem. Phys., 15, 5325–5358, https://doi.org/10.5194/acp-15-5325-2015, 2015. 
Boersma, K. F., Jacob, D. J., Eskes, H. J., Pinder, R. W., Wang, J., and van der A, R. J.: Intercomparison of SCIAMACHY and OMI tropospheric NO2 columns: Observing the diurnal evolution of chemistry and emissions from space, J. Geophys. Res., 113, 1–14, https://doi.org/10.1029/2007JD008816, 2008. 
Download
Short summary
We introduce a multi-model, multi-constituent chemical data assimilation framework that directly accounts for model error in transport and chemistry by integrating a portfolio of forward chemical transport models. The assimilation was able to reduce ensemble forward model spread and bias relative to independent measurements. Diagnostic information readily available from the framework has the potential to improve chemical predictions through relationships such as emergent constraints.
Share
Altmetrics
Final-revised paper
Preprint