Preprints
https://doi.org/10.5194/egusphere-2023-1967
https://doi.org/10.5194/egusphere-2023-1967
07 Nov 2023
 | 07 Nov 2023
Status: this preprint is open for discussion.

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, Xing Yu, and Hao Li

Abstract. Warm-sector heavy rainfall often occurs along the coast of South China, and it is usually localized and long-lasting, making it challenging to predict. High-resolution numerical weather prediction (NWP) models are increasingly used to better resolve topographic features and forecast such high-impact weather events. However, when the grid spacing becomes comparable to the length scales of convection, known as the gray zone, the turbulent eddies in the atmospheric boundary layer are only partially resolved and parameterized to some extent. Whether using a convection parameterization (CP) scheme in the gray zone remains controversial. Scale-aware CP schemes are developed to enhance the representation of convective transport within the gray zone. The multi-scale Kain-Fritsch (MSKF) scheme includes modifications that allow for its effective implementation at a grid resolution as high as 2 km. In recent years, there has been an increasing application of machine learning (ML) models to various domains of atmospheric sciences, including the replacement of physical parameterizations with ML models. This work proposes a multi-output bidirectional long short-term memory (Bi-LSTM) model as a replace the scale-aware MSKF CP scheme. The Weather Research and Forecast (WRF) model is used to generate training and testing data over South China at a horizontal resolution of 5 km. Furthermore, the WRF model is coupled with the ML based CP scheme and compared with WRF simulations with original MSKF scheme. The results demonstrate that the Bi-LSTM model can achieve high accuracy, indicating the potential use of ML models to substitute the MSKF scheme in the gray zone.

Xiaohui Zhong et al.

Status: open (until 02 Jan 2024)

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  • RC1: 'Comment on egusphere-2023-1967', Anonymous Referee #1, 05 Dec 2023 reply

Xiaohui Zhong et al.

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, Xing Yu, Hao Li 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, Xing Yu, Hao Li https://doi.org/10.5281/zenodo.10032404

WRF Model Version 4.3 William C. Skamarock, Joseph B. Klemp, Jimy Dudhia, David O. Gill, Zhiquan Liu, Judith Berner, Wei Wang, J. G. Powers, M. G. Duda, D. M. Barker, and others https://github.com/wrf-model/WRF

Xiaohui Zhong et al.

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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.