Radar-Guided Ordinal Regression for Dynamic Update of NWP Forecasts: An Integrated Machine Learning System for Urban Convective Rainfall Hazards

Urban severe convective rainfall, a major urban hazard modulated by microscale cloud processes, mesoscale storm dynamics, and synoptic-scale forcing, severely threatens the safety of coastal megacities and urban infrastructure. As a core megacity in the Yangtze River Delta (YRD) region, Shanghai is significantly affected by the combined effects of the East Asian monsoon, complex urban-topography interactions, and sea-land breeze circulations, which not only amplify the intensity and frequency of extreme rainfall events in the city but also across the YRD region. These events further trigger severe urban waterlogging, traffic congestion, and damage to urban infrastructure. To address these challenges and advance integrated urban hazard forecasting, we develop a Physics-Informed Cross-Scale Synergistic Framework (CSSF) for urban severe convective rainfall hazards, which integrates multi-band radar observations and ordinal regression to update Numerical Weather Prediction (NWP) model forecasts. The integrated system features three core components aligned with urban hazard monitoring and forecasting needs: (1) Multi-Band Radar Observation Fusion Module, which fuses S/X-band radar data from a high-density network with machine learning-based synthesis loss functions, capturing fine-scale convective features to provide observational constraints for model forecast updates; (2) Gated Vertical Information Propagation, an improved ConvLSTM architecture with skip connections and bidirectional vertical information flow, which links radar-derived convective features to the temporal evolution of NWP forecasts, ensuring physical consistency in update processes; (3) Ordinal Regression-Based Forecast Update Module, which employs ordinal regression to integrate radar observations into NWP model outputs, updating forecast results to better reflect real-time convective development and extreme rainfall characteristics in urban areas. The integrated system is validated using 2019–2023 multi-band radar data from Shanghai’s high-density monitoring network, ERA5 reanalysis data, and NWP forecasts. Validation results demonstrate that the CSSF outperforms traditional ConvLSTM models and operational NWP systems in capturing convective initiation, extreme rainfall intensity, and urban waterlogging-prone areas, primarily by using radar observations and ordinal regression to dynamically update model forecasts, bridging the gap between observation and NWP forecast. This study presents a holistic integrated system for urban severe convective rainfall hazards, highlighting the value of radar-ordinal regression integration for NWP forecast updates, and provides a transferable approach to enhance urban hazard resilience and support evidence-based disaster risk management for climate-vulnerable cities.