Enhanced Multimodal-Fusion Network for Radar Quantitative Precipitation Estimation Incorporating Relative Humidity Data

Accurate, timely, and wide-ranging radar quantitative precipitation estimation (QPE) is crucial for effective water resource management and climate research. However, the insufficient consideration of surface meteorological conditions, such as moisture information, leads to significant biases for the existing QPE methods. This article proposes a novel QPE network based on hierarchical multibranch convolutional blocks (MCB-Net). The network incorporates relative humidity (RH) data and radar data into a multimodal-fusion architecture, effectively extracting information from diverse data sources and correcting precipitation loss due to evaporation. Additionally, a series of MCB blocks are designed to replace the traditional convolutional operation with multibranch convolutional units, enabling MCB-Net to capture complicated spatial and temporal features related to precipitation. The indices including root mean square error (RMSE), mean absolute error (MAE), mean bias ratio (MBR), and correlation coefficient (CC) are adopted to evaluate the performance of the proposed method. Experimental results demonstrate that MCB-Net outperforms conventional method and other deep learning-based QPE networks, including volume-to-point CNN network, U-Net, and ResNet. The visualized results illustrate that the proposed network can effectively mitigate precipitation overestimation and enhance the accuracy of precipitation estimation.