Local Climate

Local climate research focuses on understanding and predicting highly localized variations in climate patterns, going beyond large-scale global models to provide more precise regional and urban-scale insights. Current research employs advanced machine learning techniques, including diffusion models, XGBoost, and contrastive learning GANs, to improve the accuracy and resolution of climate projections, often incorporating remote sensing data like LiDAR and satellite imagery. These advancements are crucial for enhancing climate change adaptation strategies, enabling more accurate risk assessments for localized hazards such as heatwaves, droughts, and floods, and informing effective mitigation policies at the community level. Improved downscaling methods are particularly important for addressing data scarcity in less-developed regions.