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.

南中国沿海经常发生局地且持续时间较长的暖区强降雨，预测其仍然具有挑战性。本研究提出了一个基于多输出双向长短期记忆（Bi-LSTM）模型的新的替代尺度感知 MSKF CP 方案，实验结果表明 Bi-LSTM 模型能够高精度地预测南中国地区的天气情况，并有望在灰区替代 MSKF 方案。