Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 22
Articles | Volume 17, issue 22
https://doi.org/10.5194/gmd-17-8267-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-8267-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 22
Methods for assessment of models
 | 
25 Nov 2024
Methods for assessment of models |  | 25 Nov 2024

Observational operator for fair model evaluation with ground NO2 measurements

Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao

Supplement

https://doi.org/10.5194/gmd-17-8267-2024-supplement

Data sets

Ground-based air quality monitoring measurements China Ministry of Environmental Protection http://www.cnemc.cn/en/

Land use information and NO$_2$ observations of environmental monitoring stations in China L. Fang https://doi.org/10.5281/zenodo.10052537

Outputs of NO$_2$ from GEOS-Chem v13.4.0 L. Fang https://doi.org/10.5281/zenodo.10989700

Model code and software

geoschem/GCClassic: GEOS-Chem 13.4.0 The International GEOS-Chem User Community https://doi.org/10.5281/zenodo.6511970

Python source code of the LUBR observational operator L. Fang https://doi.org/10.5281/zenodo.10052513

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Short summary
Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
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