Towards high-fidelity urban wind profiles for the built environment: a neural field to fuse multi-source observational data in Guangzhou, China

Accurate urban wind analysis is critically hampered by sparse and heterogeneous observational data. This work presents a solution through NF-MW (stands for Neural Field for Multi-source Winds), a model that fuses data from Doppler LiDAR and wind profiler radar into a continuous high-resolution wind field. By learning a direct mapping from spatio-temporal coordinates to wind values, NF-MW can reconstruct wind speed and direction at any arbitrary height and time. The framework uniquely handles the 360^∘ periodicity of wind direction and uses Fourier-enriched features to capture high-frequency gusts and turbulence often missed by other models. In a Guangzhou case study, NF-MW achieved a Mean Absolute Error of 0.55 m/s for wind speed and 8.95^∘ for wind direction, demonstrating superior accuracy over traditional methods. This approach provides the building and environment community with a robust method to generate the realistic dynamic wind data essential for applications ranging from pedestrian comfort assessments to urban air quality modeling.