A Comparative Evaluation of Advanced Urban Data Methods in WRF

Urban parameterization is critical for accurately simulating near-surface temperature and the Urban Heat Island (UHI) effect in WRF. In this study, we compare three distinct approaches—W2W (a Python package integrating WUDAPT LCZ data), WRFUP (a Python package leveraging global high-resolution datasets), and a LiDAR-based parameterization—during the August, 2023 heatwave in Grenoble, France. Our analysis demonstrates that WRFUP improves upon W2W by capturing finer-scale urban morphology, thereby reducing nighttime temperature overestimation and more accurately representing the spatial structure of the UHI. LiDAR-based parameterization remains the most precise for depicting detailed urban geometries, however its high computational cost and limited availability hinder large-scale applications and replicability. Systematic biases are identified, indicating that model deficiencies extend beyond the accuracy of urban data alone. These findings underscore the benefits of high-resolution urban datasets in WRF simulations, while also highlighting the need for further advancements in urban sur- face energy modeling.