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Published September 5, 2023 | Version 1
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Global Surface Ozone Concentration Dataset 1990-2017 Mapped at Fine Resolution through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output

Authors/Creators

  • 1. Environmental Sciences and Engineering, University of North Carolina at Chapel Hill
  • 2. Cooperative Institute for Research in Environmental Sciences, University of Colorado, ; NOAA Chemical Sciences Laboratory
  • 3. Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich
  • 4. Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University
  • 5. Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University,
  • 6. Meteorological Research Institute (MRI), Tsukuba, Japan
  • 7. Centre National de Recherches Météorologiques, Université de Toulouse, Météo-France, CNRS, Toulouse, France
  • 8. NASA Goddard Space Flight Center,; Universities Space Research Association
  • 9. National Center for Atmospheric Research
  • 10. NOAA Geophysical Fluid Dynamics Laboratory; Program in Atmospheric and Oceanic Sciences, Princeton University
  • 11. NASA Goddard Space Flight Center; Universities Space Research Association
  • 12. Graduate School of Environmental Studies, Nagoya University,; Japan Agency for Marine-Earth Science and Technology (JAMSTEC)
  • 13. NOAA Geophysical Fluid Dynamics Laboratory; Program in Atmospheric and Oceanic Sciences, Princeton University; Department of Meteorology and Atmospheric Science, Pennsylvania State University

Description

This global surface ozone concentration dataset corresponds to the data developed in this paper:

DeLang, M. N., J. S. Becker, K.-L. Chang, M. L. Serre, O. R. Cooper, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, S. Cleland, E. Collins, M. Brauer, and J. J. West (2021) Mapping yearly fine resolution global surface ozone through the Bayesian Maximum Entropy data fusion of observations and model output for 1990-2017, Environmental Science & Technology, 55, 4389-4398, doi: 10.1021/acs.est.0c07742.

Ozone concentrations are estimated as described in the paper, with output shown for the Ozone Season Daily Maximum 8-hr metric (OSDMA8) for each year between 1990 and 2017, at 0.1 degree spatial resolution.  Ozone is estimated through data fusion of output from several global models, with observations of ozone collected by TOAR.  The data fusion involves application of the M3Fusion method to create a multi-model composite of several global models, followed by BME data fusion, as described in the paper.  

The *.nc file contains the latitude, longitude, ozone concentration estimate, and estimated variance for each 0.1 x 0.1 degree grid cell.

Please contact Jason West (jasonwest@unc.edu) with questions about the dataset.  We'd like to hear from you to know how you're using the data!

 

 

Notes

Please contact Jason West (jasonwest@unc.edu) with questions about the dataset. We would like to hear from you about how you're using the data! We acknowledge support from the NASA Health and Air Quality Applied Sciences Team (#NNX16AQ30G) and the National Institute for Occupational Safety and Health (T42-OH008673). M.D. was supported by the Japan Society for the Promotion of Science (JP20K04070).

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