Machine learning significantly improves the simulation of hourly-to-yearly scale cloud nuclei concentration and radiative forcing in polluted atmosphere

Ren, Jingye; Zou, Songjian; Xu, Honghao; Liu, Guiquan; Wang, Zhe; Zhang, Anran; Zhao, Chuanfeng; Hu, Min; Shang, Dongjie; Tang, Lizi; Huang, Ru-Jin; Sun, Yele; Zhang, Fang

doi:10.5194/gmd-19-6403-2026

Articles | Volume 19, issue 14

https://doi.org/10.5194/gmd-19-6403-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-19-6403-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 14

Development and technical paper

|

17 Jul 2026

Development and technical paper |

| 17 Jul 2026

Machine learning significantly improves the simulation of hourly-to-yearly scale cloud nuclei concentration and radiative forcing in polluted atmosphere

Jingye Ren, Songjian Zou, Honghao Xu, Guiquan Liu, Zhe Wang, Anran Zhang, Chuanfeng Zhao, Min Hu, Dongjie Shang, Lizi Tang, Ru-Jin Huang, Yele Sun, and Fang Zhang

Supplement

https://doi.org/10.5194/gmd-19-6403-2026-supplement

Data sets

Machine learning significantly improves the simulation of hourly-to-yearly scale cloud nuclei concentration and radiative forcing in polluted atmosphere Jingye Ren et al. https://doi.org/10.5281/zenodo.18932004

NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999 NCEP https://doi.org/10.5065/D6M043C6

Model code and software

Machine learning significantly improves the simulation of hourly-to-yearly scale cloud nuclei concentration and radiative forcing in polluted atmosphere Jingye Ren et al. https://doi.org/10.5281/zenodo.18932004

A Description of the Advanced Research WRF Model Version 4.1 (https://www2.mmm.ucar.edu/wrf/users/download/get_source.html) W. Skamarock et al. https://doi.org/10.5065/1dfh-6p97

Short summary

In this study, a new framework of cloud condensation nuclei (CCN) prediction in polluted region has been developed and it achieves well prediction of hourly-to-yearly scale across North China Plain. The study reveals the machine learning model can largely reduce the uncertainty in simulating cloud radiative forcing, illustrating the high sensitivity of climate forcing to changes in CCN. This improvement of our new model would be helpful to aerosols climate effect assessment in models.