WRFUP: WRF Urban Parameters Python Package

Accurate urban parameterizations in mesoscale models are essential to simulate spatial distribution of air temperature and heat stress in urban areas. To achieve accuracy, high-quality urban data is key. Existing approaches, however, have major limitations. LiDAR-based methods, while highly accurate, require extensive processing and are impractical for large-scale applications. WUDAPT-based approaches (World Urban Database and Access Portal Tools) are easier to implement but rely on generalized classifications that fail to capture fine-scale urban heat patterns, leading to significant temperature biases. This study presents WRFUP (WRF Urban Parameters), a Python tool that automates the ingestion of high-resolution global urban morphology datasets into WRF, making accurate urban modeling significantly easier. WRFUP computes building height, urban fraction, and surface fraction using globally available datasets such as World Settlement Footprint 3D (90m resolution) and Global Urban Fraction (100m resolution) and a custom Machine Learning-based dataset for building surface fraction (CitySurfAce, 100 m). Using WRF with the Building Effect Parameterization (BEP), the Building Energy Model (BEM), and the COMFORT module, along with a high-density urban temperature measurement network over the Grenoble area (France), we demonstrate that WRFUP achieves results as accurate as LiDAR-based methods, while being far simpler to use. Compared to WUDAPT-based parameterization, WRFUP significantly improves spatial temperature and heat stress distributions and reduces nighttime temperature overestimation, offering a practical balance between accuracy and computational efficiency. These findings establish WRFUP as an accessible, high-accuracy alternative for urban parameterization in WRF, bridging the gap between oversimplified WUDAPT classifications and complex LiDAR data processing. By providing a streamlined workflow, WRFUP opens the door to more accessible urban climate modeling, improving predictions of urban heat island intensity, heat stress, and energy demand in cities.