Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 11
Articles | Volume 19, issue 11
https://doi.org/10.5194/gmd-19-4835-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-4835-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 11
Development and technical paper
 | 
10 Jun 2026
Development and technical paper |  | 10 Jun 2026

OIRF-LEnKF v1.0: a novel data assimilation system by integrating incremental machine learning with a localized EnKF for enhanced PM2.5 chemical component simulation and reanalysis

Hongyi Li, Ting Yang, Lei Kong, Di Zhang, Guigang Tang, Xiao Tang, and Zifa Wang

Supplement

https://doi.org/10.5194/gmd-19-4835-2026-supplement

Data sets

OIRF-LEnKF v1.0 Hongyi Li and Ting Yang https://doi.org/10.5281/zenodo.17346786

ERA5 hourly data on pressure levels from 1940 to present H. Hersbach et al. https://doi.org/10.24381/cds.bd0915c6

OIRF-LEnKF v1.0 related open-access datasets Hongyi Li https://doi.org/10.5281/zenodo.17359290

High-resolution Air Quality Reanalysis Dataset over China (CAQRA) X. Tang et al. https://doi.org/10.11922/sciencedb.00053

NAQPMS-PDAF v2.0 (Version 2.0) Hongyi Li et al. https://doi.org/10.5281/zenodo.10886914

CAMS global reanalysis (EAC4) Copernicus Atmosphere Monitoring Service https://doi.org/10.24381/d58bbf47

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Short summary
We develop a novel data assimilation system by integrating incremental machine learning with a localized ensemble Kalman filter to enhance the simulation and reanalysis of particulate matter < 2.5 µm chemical components. Compared to traditional chemical transport model-based data assimilation, our assimilation system has superior computational efficiency and simulation improvements. Comparisons with independent observations and reanalysis datasets validate the robust performance of our system.
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