Lightweight models improve finer scale climate data prediction accuracy
Lightweight Probabilistic Downscaling from a Deterministic Base Model
Machine Learning
Summary
Climate models often predict weather and climate at a large scale, but it's useful to have detailed local predictions too. The authors developed smaller, efficient machine learning models to increase the detail of climate predictions, working from coarse global data. They blended two training steps—starting with simple prediction and then tuning for uncertainty—to get better, more accurate results across regions like the Alps and South Africa. Their approach balances accuracy and computational cost better than previous methods.
What this means in practice
- •For climate modelers: Provide more accurate regional climate predictions from global data with lightweight probabilistic models that reduce computational demand.
- •For environmental risk analysts: Use improved fine-scale climate data to better assess localized weather hazards and temperature extremes across diverse regions.
Authors
Joseph McLean, Tiffany Vlaar, Sigrid Passano Hellan, Linus Ericsson
Abstract
Climate data downscaling is the task of increasing the spatial resolution of climate data, typically by generating fine-resolution regional climate data from coarse global model output. Recent machine learning (ML) work in the related task of weather forecasting has seen significant improvements due to newly devised training methods and architectural components, but these have not yet benefited downscaling. We adapt two of these methods to create a family of lightweight probabilistic ML downscaling models built on a modified U-Net backbone and evaluate them on the CORDEX-ML-Bench suite for daily maximum temperature and precipitation across three geographic regions: the Alps, New Zealand and South Africa. We find that a two-stage training curriculum, combining deterministic pretraining with probabilistic tuning, transfers well to downscaling, beating the state-of-the-art for RMSE. Our work provides an advancement towards lightweight, probabilistic downscaling models, reducing the current trade-off between computational intensity and distributional fit.