Urban Air Temperature Prediction using Conditional Diffusion Models

Abstract

Urbanization as a global trend has led to many environmental challenges, including the urban heat island (UHI) effect. The increase in temperature has a significant impact on the well-being of urban residents. Air temperature (T_a) at 2m above the surface is a key indicator of the UHI effect. How land use land cover (LULC) affects T_a is a critical research question which requires high-resolution (HR) T_a data at neighborhood scale. However, weather stations providing T_a measurements are sparsely distributed e.g. more than 10km apart; and numerical models are impractically slow and computationally expensive. In this work, we propose a novel method to predict HR T_a at 100m ground separation distance (gsd) using land surface temperature (LST) and other LULC related features which can be easily obtained from satellite imagery. Our method leverages diffusion models for the first time to generate accurate and visually realistic HR T_a maps, which outperforms prior methods. We pave the way for meteorological research using computer vision techniques by providing a dataset of an extended spatial and temporal coverage, and a high spatial resolution as a benchmark for future research. Furthermore, we show that our model can be applied to urban planning by simulating the impact of different urban designs on T_a.

Tasks

Reproductions