Physics-Constrained Deep Learning for Climate Downscaling

Abstract

The availability of reliable, high-resolution climate and weather data is important to inform long- term decisions on climate adaptation and mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational costs and therefore often predict quantities at a coarse spatial resolution. Statistical downscaling can provide an efficient method of upsampling low-resolution data. In this field, deep learning has been applied successfully, often using methods from the super-resolution domain in computer vision. Despite achieving visually compelling results, such models often violate conservation laws when predicting physical variables.