Deep Learning for Urban Microclimate Downscaling: From Coarse WRF Data to Building-Resolved PALM Simulations

Accurate high-resolution urban microclimate modeling, including wind and temperature prediction, is essential for urban planning, occupant comfort analysis, and building energy efficiency optimization. However, traditional computational fluid dynamics (CFD) methods are computationally expensive and time-intensive for applications requiring rapid urban microclimate estimation. This work presents a novel deep learning framework that directly downscales kilometer-scale Weather Research and Forecasting (WRF) model outputs to a 10-meter level resolution 3D urban microclimate for a given geographical setting. By incorporating building geometries as model inputs, our approach captures fine-scale building-induced effects in urban wind and temperature fields, which are absent in WRF's coarse-resolution outputs.

The deep learning model is trained and evaluated using urban microclimate data simulated with PALM for a realistic geographical setting in Berlin, Germany, where one week's worth of low-resolution WRF outputs serve as boundary conditions. Our proposed approach follows a two-stage training process. First, a conditional neural field (CNF) encodes the coarse WRF boundary conditions and generates a smooth, building-agnostic 3D flow field at PALM resolution. Next, a geometry-aware Fourier neural operator (FNO) refines this field by incorporating high-resolution building geometries, accurately capturing the complex interactions between airflow and urban structures. To effectively represent complex building geometries, we introduce a multi-directional distance feature (MDDF) that captures long-range spatial relationships between buildings. 

By producing building-resolved microclimate data from WRF outputs in near-real-time, our approach facilitates applications that are otherwise impractical with conventional CFD solvers. Despite being trained on a limited set of WRF boundary conditions, our model generalizes effectively to unseen conditions, underscoring its potential as a powerful and flexible tool for rapid urban microclimate forecasting and analysis.