Data assimilation integrates real-time observational data into climate models, improving forecast accuracy and responsiveness. In urban climate modelling, this approach is vital for correcting biases, refining predictions, and capturing fast-evolving processes like heat waves or localized storms. Despite advances, there are still gaps in effectively assimilating data from heterogeneous urban sources, including satellite, drone, and sensor networks, while also ensuring validation and consistency across models.

We encourage contributions that demonstrate novel data assimilation techniques, especially those improving the accuracy of urban heat and flood predictions, integrating diverse datasets, and real-time forecasting. Submissions that focus on assimilation in high-resolution urban models and enhancing extreme weather forecasting in cities are particularly encouraged. Topics of interest are novel data assimilation methods for urban models, integration of multi-source urban datasets (e.g., sensor networks, satellite, drones), real-time forecasting of urban heat and flood events, data assimilation for improving extreme weather event response, etc.