A technique for nowcasting heavy rainfall locations using salient object detection algorithm based on deep learning network

For precipitation with the same duration, the higher the precipitation intensity, the greater the occurrence likelihood of urban waterlogging, flash flooding, mudslides, landslides, and other secondary disasters. In the context of global warming, sub-hourly extreme precipitation events are increasing much faster than those with longer durations, highlighting the need for advanced monitoring and nowcasting technologies for sub-hourly heavy rainfall. This study selects 30 heavy precipitation events in Chongqing from 2013 to 2021 to train the deep network U²-Net for significant object detection, using weather radar reflectivity mosaics as inputs. The network identifies heavy rainfall areas as salient objects and autonomously learns the nonlinear relationship between the spatial distribution of reflectivity at a given time and the subsequent 30 min heavy rainfall areas and provides forecasts of heavy rainfall region. The sample labels are divided into three categories based on thresholds of 10 mm, 20 mm, and 30 mm obtained from radar-rain gauge quantitative precipitation estimates. The model inputs are radar reflectivity mosaics at altitudes of 3 km, 4.5 km, and 7 km. After training and validation, three forecasting models corresponding to the three heavy rainfall thresholds are developed. Testing on an independent dataset reveals that, with a neighborhood radius of 5 km, the models achieve hit rates of 0.66, 0.73, and 0.72, false alarm rates of 0.06, 0.32, and 0.57, and critical success indices of 0.64, 0.54, and 0.37 for the 10 mm, 20 mm, and 30 mm thresholds, respectively. Higher probabilities in the forecast maps indicate more reliable forecasts of heavy rainfall areas. In conclusion, the heavy rainfall forecasting models can effectively predict 30 min heavy rainfall areas by extracting multi-scale features of three-dimensional radar reflectivity. These models help supplement rainfall monitoring and forecasting in areas with sparse rain gauge networks, providing a valuable reference for nowcasting technologies that require multi-source data feature extraction.