Multimodal Deep Learning Framework for Urban Air Temperature Forecasting: Bridging Local and Synoptic Scales

Accurate air temperature (T_a)  forecasting in urban areas is crucial for various socio-economic aspects, including risk warning and optimization of electricity systems. However, forecasting within urban environments faces substantial challenges due to the coarse spatial resolution and inadequate urban representation in numerical weather prediction (NWP) models. In this study, we present a novel multimodal deep learning framework that learns local dynamics from ground-level weather stations while effectively informing large-scale weather patterns for short-range (1- 24  hour lead time) T_a forecasting. The framework first employs graph neural networks (GNNs) to model intra-city spatiotemporal dynamics across 35 weather stations, achieving over 12% forecast improvement compared to modeling individual time series, primarily through mean state regularization. We further develop an end-to-end multimodal framework by integrating the GNN with synoptic weather patterns, achieving an additional 23% improvement, with particular expertise in winter and capturing cold spell events. Our study demonstrates the effectiveness of incorporating multi-scale information from diverse data sources and reveals that weather patterns within approximately 2000 km are critical for local city-scale forecasting. This framework can be readily adapted to other urban areas and will benefit significantly from the increasing deployment of smart IoT sensors to effectively address intra-city temperature heterogeneity.