Assessing the Impact of Incorporating Bias-Adjusted
14 affiliations
- 1Department of Environmental Sciences & Engineering
- 2University of North Carolina
- 3Cooperative Institute for Research in Environmental Sciences
- 4University of Colorado
- 5NOAA Chemical Sciences Laboratory
- 6Jet Propulsion Laboratory
- 7California Institute of Technology
- 8NSF National Center for Atmospheric Research
- 9Univ. Paris Est Créteil and Université de Paris Cité, CNRS, LISA
- 10Meteorological Research Institute
- 11NOAA Geophysical Fluid Dynamics Laboratory
- 12Department of Marine, Earth and Atmospheric Sciences
- 13North Carolina State University
- 14NASA Goddard Space Flight Center
Abstract
Understanding global ground-level ozone is hindered by significant monitoring gaps. To address this, we introduce a Bayesian Maximum Entropy data fusion framework that includes satellite ozone observations, generating a global 0.1° surface ozone dataset (2005–2022). Satellite observations (IASI+GOME2, OMI/MLS), adjusted to ground-level estimates, were analyzed using Flexible Regionalized Air Quality Model Performance to correct biases and estimate variance. Ground observations (hard data) and satellite-derived estimates (soft data) were then integrated, 2 using a composite of 10 atmospheric chemistry models and 3 reanalyses as the global spatialtemporal trend. Cross-validations demonstrated high overall accuracy (R2 = 0.83; RMSE = 3.73 ppb). Although integrating satellite-derived soft data yields only marginal performance gains in densely monitored areas, it substantially reduces model uncertainties and improves model accuracy across large spatial gaps. Notably, for the furthest unmonitored gap group at a 20° checkerboard, including satellite data increased R2 by 25.9% and decreased RMSE by 21.1% after the integration. Applying this optimized data fusion configuration from 2005 to 2022, global population-weighted ozone is estimated to have increased by 0.21 ppb/decade. Consequently, the global population exposed to ozone levels exceeding 50 ppb more than doubled over this 18-year period, rising from 17.2% to 41.0%.
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BibTeX
@misc{wang2026assessing,
title = {{Assessing the Impact of Incorporating Bias-Adjusted}},
author = {Hantao Wang and Marc L. Serre and Kai-Lan Chang and Praful Dodda and George E. Heidkamp and Kazuyuki Miyazaki and Rebecca R. Buchholz and Juan Cuesta and Makoto Deushi and Meiyun Lin and Zhen Qu and Luke D. Oman and Jerry R. Ziemke and Owen R. Cooper and J. Jason West},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15011001/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011001/v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Assessing the Impact of Incorporating Bias-Adjusted
AU - Hantao Wang
AU - Marc L. Serre
AU - Kai-Lan Chang
AU - Praful Dodda
AU - George E. Heidkamp
AU - Kazuyuki Miyazaki
AU - Rebecca R. Buchholz
AU - Juan Cuesta
AU - Makoto Deushi
AU - Meiyun Lin
AU - Zhen Qu
AU - Luke D. Oman
AU - Jerry R. Ziemke
AU - Owen R. Cooper
AU - J. Jason West
PY - 2026
DA - 2026/10/25
PB - ChemRxiv
DO - 10.26434/chemrxiv.15011001/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011001/v1
AB - Understanding global ground-level ozone is hindered by significant monitoring gaps. To address this, we introduce a Bayesian Maximum Entropy data fusion framework that includes satellite ozone observations, generating a global 0.1° surface ozone dataset (2005–2022). Satellite observations (IASI+GOME2, OMI/MLS), adjusted to ground-level estimates, were analyzed using Flexible Regionalized Air Quality Model Performance to correct biases and estimate variance. Ground observations (hard data) and satellite-derived estimates (soft data) were then integrated, 2 using a composite of 10 atmospheric chemistry models and 3 reanalyses as the global spatialtemporal trend. Cross-validations demonstrated high overall accuracy (R2 = 0.83; RMSE = 3.73 ppb). Although integrating satellite-derived soft data yields only marginal performance gains in densely monitored areas, it substantially reduces model uncertainties and improves model accuracy across large spatial gaps. Notably, for the furthest unmonitored gap group at a 20° checkerboard, including satellite data increased R2 by 25.9% and decreased RMSE by 21.1% after the integration. Applying this optimized data fusion configuration from 2005 to 2022, global population-weighted ozone is estimated to have increased by 0.21 ppb/decade. Consequently, the global population exposed to ozone levels exceeding 50 ppb more than doubled over this 18-year period, rising from 17.2% to 41.0%.
ER -