A Radar Echo Extrapolation Method Based on the ARUR Network with Multi-Source Data

Accurate nowcasting provides key information for disaster weather warnings. Nowcasting is mostly based on radar echo extrapolation, where the echo evolution results from complex interactions among cloud systems and various thermal–dynamic features of the weather background. However, existing research has not integrated high spatiotemporal resolution weather background information. In this study, multiple data fusion units (Radarcells) integrating the radar mosaic and rapid-refresh data are constructed as input for the radar echo extrapolation network architecture designed according to Attention-Resnet Unet with Radarcells (ARUR). In addition, a self-defined loss function combining weighted mean square error and structural similarity index is further proposed to improve the extrapolation effect. The rapid-refresh data includes relative humidity, zonal wind, meridional wind, and vertical velocity within the range from 115.48 to 117.48°E, 38.81 to 40.81°N during June–August from 2018 to 2021. Four ARUR-based models with different Radarcells as input and an ARU-based model (Attention-Resnet Unet, without fusing physical data) are trained for 120 min of extrapolation, respectively. The models are evaluated with the indicators, including critical success index (CSI), probability of detection (POD), and false alarm rate (FAR), by the test dataset. The results show that the performance of echo prediction and timeliness by ARUR-based models is better than the ARU-based model, especially for strong echo prediction. Under the reflectivity thresholds of 25 and 35 dBZ, the average values of CSI, FAR, and POD calculated by ARUR-based models are improved by 8.42%, 7.76%, 8.52% and 10.36%, 7.36 %, 9.10% than those by ARU-based, respectively. The study suggests that integrating weather background information can significantly enhance the effect of extrapolation by means of improving the issues of echo blurriness, formation, and dissipation compared with the previous deep learning-based models.