Developing hybrid precipitation nowcasting model with WRF and conditional GAN-based model

Physical process-based numerical prediction models (NWPs) and radar-based probabilistic methods have been mainly used for short-term precipitation prediction. Recently, radar-based precipitation nowcasting models using advanced machine learning (ML) have been actively developed. Although the ML-based model shows outstanding performance in short-term rainfall prediction, it significantly decreases performance due to increased lead time. It has the limitation of being a black box model that does not consider the physical process of the atmosphere. To address these limitations, we aimed to develop a hybrid precipitation nowcasting model, which combines NWP and an advanced ML-based model via an ML-based ensemble method. The Weather Research and Forecasting (WRF) model was used as NWP to generate a physics-based rainfall forecast. In this study, we developed the ML-based precipitation nowcasting model with conditional Generative Adversarial Network (cGAN), which shows high performance in the image generation tasks. The radar reflectivity data, WRF hindcast meteorological outputs (e.g., temperature and wind speed), and static information of the target basin (e.g., DEM, Land cover) were used as input data of cGAN-based model to generate physics-informed rainfall prediction at the lead time up to 6 hours. The cGAN-based model was trained with the data for the summer season of 2014-2017. In addition, we proposed an ML-based blending method, i.e., XGBoost, that combines cGAN-based model results and WRF forecast results. To evaluate the hybrid model performance, we analyzed the performance of precipitation predictions on three heavy rain events in South Korea. The results confirmed that using the blending method to develop a hybrid model could provide an improved precipitation nowcasting approach.

 

Acknowledgements

 This work was supported by a grant from the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (2020R1A2C2007670).