Articles | Volume 19, issue 17
https://doi.org/10.5194/gmd-19-8367-2026
https://doi.org/10.5194/gmd-19-8367-2026
Methods for assessment of models
 | 
09 Sep 2026
Methods for assessment of models |  | 09 Sep 2026

Leveraging JEDI for atmospheric composition: a unified framework for evaluating observations and model forecasts

Shih-Wei Wei, Jérôme Barré, Soyoung Ha, Maryam Abdi-Oskouei, Benjamin Ménétrier, Cheng Dang, and Cheng-Hsuan Lu

Data sets

Sample input and output data of "Leveraging JEDI for Atmospheric Composition: A unified framework for evaluating observations and model forecasts" Shih-Wei Wei et al. https://doi.org/10.5281/zenodo.17058099

TEMPO NO2 tropospheric and stratospheric columns V03 (PROVISIONAL) X. Liu https://doi.org/10.5067/IS-40E/TEMPO/NO2_L2.003

TROPOMI Level 2 Nitrogen Dioxide total column products Copernicus Sentinel-5P https://doi.org/10.5270/S5P-9bnp8q8

MODIS Atmosphere L2 Aerosol Product, NASA MODIS Adaptive Processing System R. Levy et al. https://doi.org/10.5067/MODIS/MOD04_L2.061

MERRA-2 inst3_3d_asm_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Assimilated Meteorological Fields V5.12.4 Global Modeling and Assimilation Office (GMAO) https://doi.org/10.5067/WWQSXQ8IVFW8

MERRA-2 inst3_3d_aer_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Aerosol Mixing Ratio V5.12.4 Global Modeling and Assimilation Office (GMAO) https://doi.org/10.5067/LTVB4GPCOTK2

VIIRS/SNPP Dark Target Aerosol L2 6-Min Swath 6 km VIIRS Atmosphere Science Team https://doi.org/10.5067/VIIRS/AERDT_L2_VIIRS_SNPP.002

VIIRS/NOAA20 Dark Target Aerosol 6-Min L2 Swath 6 km VIIRS Atmosphere Science Team https://doi.org/10.5067/VIIRS/AERDT_L2_VIIRS_NOAA20.002

VIIRS/SNPP Deep Blue Aerosol L2 6-Min Swath 6 km VIIRS Atmosphere Science Team https://doi.org/10.5067/VIIRS/AERDB_L2_VIIRS_SNPP.002

VIIRS/NOAA20 Deep Blue Aerosol L2 6-Min Swath 6 km VIIRS Atmosphere Science Team 0.5067/VIIRS/AERDB_L2_VIIRS_NOAA20.002

Model code and software

JEDI-ACE Shih-Wei Wei et al. https://github.com/weiwilliam/JEDI-ACE

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Short summary
This paper presents a flexible workflow using a unified data assimilation framework to evaluate atmospheric composition models. It enables comparison of observations with forecasts of trace gases and aerosols from different models. The system is consistent and adaptable, reducing repetitive work, supporting model validation and observation assessment, and aligning evaluation with operational data assimilation for research and practical applications.
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