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

Data sets

Machine Learning Parameterization of the Multi-scale Kain-Fritsch (MSKF) Convection Scheme and stable simulation coupled in WRF using WRF-ML v1.0 Xiaohui Zhong et al. https://doi.org/10.5281/zenodo.10032404

Model code and software

Machine Learning Parameterization of the Multi-scale Kain-Fritsch (MSKF) Convection Scheme and stable simulation coupled in WRF using WRF-ML v1.0 Xiaohui Zhong et al. https://doi.org/10.5281/zenodo.10032404

The source codes of WRF-V4.3 and WPS-V4.3 Xiaohui Zhong https://doi.org/10.5281/zenodo.11112400

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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.
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