Deep Learning for Radar Quantitative Precipitation Estimation over Complex Terrain in Southern China

Developing region-specific radar quantitative precipitation estimation (QPE) products for South China (SC) is crucial due to its unique climate and complex terrain over there. Deep learning (DL) has emerged as a promising avenue for radar QPE, especially graph neural networks (GNNs). Many studies have tested the DL models in radar QPE, but virtually no studies have evaluated the performance of DL models in different precipitation intensities, types, or organizations. Moreover, limited attention has been given to whether DL-based methods can mitigate radar QPE errors caused by orographic influences in complex terrains, such as those in SC.

This study investigates the advantages of DL methods for QPE tasks in South China, utilizing nearly three years of hourly gauge data as labels and ground-based radar reflectivity as inputs. Firstly, multi-layer perceptron (MLP), Convolutional Neural Networks (CNNs), and GNNs with similar architectures are constructed and compared to traditional Z-R relationships considering precipitation types. DL methods outperform traditional Z-R relationships and GNNs perform the best. More importantly, this study conducts a systematic evaluation of the proposed GNN. For extreme precipitation (>30 mm/h), GNN achieves the smallest MAE, highlighting its potential for hazardous event estimation. It also demonstrates stable performance for stratiform and organized precipitation, with minimal bias and standard deviation. However, GNN is less effective for isolated precipitation, whereas CNNs are a better choice due to their ability to estimate scattered rainfall accurately. Last but not least, the Z-R relationship shows systematic spatial biases, overestimating precipitation in coastal plains and underestimating it in inland high-altitude regions. DL methods alleviate these terrain-induced biases by incorporating spatial information. Overall, this study highlights the advantages of DL methods across different precipitation scenarios and demonstrates their ability to mitigate systematic biases from complex terrain.