Radar-Based Assessment of Consecutive Extreme Rainfall Events in Subtropical Monsoon Regions
Keywords:
Radar precipitation, clustering, DBSCAN, CNN, monsoon rainfallAbstract
This study provides a high-resolution assessment of consecutive extreme rainfall events in subtropical monsoon regions using weather radar datasets (1 km spatial resolution, 5-minute temporal resolution, 2010–2022, approximately 3.1 billion grid–time observations). A spatiotemporal clustering framework based on density-based spatial clustering of applications with noise (DBSCAN) is employed to identify coherent rainfall event sequences, followed by a convolutional neural network (CNN) to classify intensity regimes. Extreme events are defined using a dynamic percentile threshold (top 5% of local rainfall intensity). Results indicate that 31% of extreme rainfall events occur in clusters lasting longer than 72 hours, with a mean cluster duration of 64.2 hours and a standard deviation of 18.7 hours. The CNN model achieves an overall classification accuracy of 90.1%, with a macro-averaged F1-score of 0.87 across intensity categories. Coastal regions exhibit 1.9 times higher clustering frequency than inland regions, primarily associated with sustained moisture flux convergence exceeding 120 kg·m⁻¹·s⁻¹. Temporal autocorrelation analysis reveals a persistence memory of 8–14 hours in rainfall intensity fields, indicating strong mesoscale organization. These findings highlight the critical role of spatiotemporal clustering and moisture transport in shaping extreme rainfall sequences in monsoon environments
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