Reproducing urban wind field during the typhoon period via multi-source data assimilation in WRF-3DVAR

Abstract

This study employs a Weather Research and Forecasting model with 3-dimensional variational data assimilation (WRF-3DVAR) to integrate multi-source meteorological data from satellites, Light Detection and Ranging (LiDAR), and surface weather stations, introducing multi-scale observational constraints to reproduce the urban wind field during typhoon Yagi. The research findings are as follows: satellite radiance assimilation calibrates the typhoon track at the mesoscale; surface weather stations add physical constraints on the near-surface wind field, compensating for deficiencies in the model’s description of underlying surface characteristics; and LiDAR-derived vertical wind profiles refine the vertical momentum exchange processes within the typhoon boundary layer, significantly improving the simulation accuracy of near-surface wind profiles. After independent validation using high wind speed stations (GI, NGP, NLS, PEN, TMS, and YTS) and LiDAR data from North District North East, demonstrated that the WRF-3DVAR assimilating multi-source data can accurately reproduce the near-surface wind speed and wind profile evolution in urban areas. The contribution of this study is to provide a new ground-space-sky data assimilation framework for reproducing urban 3D wind field, improving weather warning systems, and formulating long-term disaster prevention and mitigation strategies, effectively strengthening the city’s resilience against super typhoons.