Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 9
Articles | Volume 17, issue 9
https://doi.org/10.5194/gmd-17-3667-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-3667-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 9
Development and technical paper
 | 
07 May 2024
Development and technical paper |  | 07 May 2024

Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0

Xiaohui Zhong, Xing Yu, and Hao Li

Viewed

Total article views: 5,429 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
4,045 1,245 139 5,429 191 304
  • HTML: 4,045
  • PDF: 1,245
  • XML: 139
  • Total: 5,429
  • BibTeX: 191
  • EndNote: 304
Views and downloads (calculated since 07 Nov 2023)
Cumulative views and downloads (calculated since 07 Nov 2023)

Viewed (geographical distribution)

Total article views: 5,429 (including HTML, PDF, and XML) Thereof 5,217 with geography defined and 212 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 May 2026
Download
Short summary
In order to forecast localized warm-sector rainfall in the south China region, numerical weather prediction models are being run with finer grid spacing. The conventional convection parameterization (CP) performs poorly in the gray zone, necessitating the development of a scale-aware scheme. We propose a machine learning (ML) model to replace the scale-aware CP scheme. Evaluation against the original CP scheme has shown that the ML-based CP scheme can provide accurate and reliable predictions.
Read more
Share