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

Viewed

Total article views: 10,403 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
9,135 964 304 10,403 21 184 274
  • HTML: 9,135
  • PDF: 964
  • XML: 304
  • Total: 10,403
  • Supplement: 21
  • BibTeX: 184
  • EndNote: 274
Views and downloads (calculated since 11 Sep 2025)
Cumulative views and downloads (calculated since 11 Sep 2025)

Viewed (geographical distribution)

Total article views: 10,403 (including HTML, PDF, and XML) Thereof 10,349 with geography defined and 54 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 28 Jul 2026
Download
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.
Read more
Share