Self-Supervised Vision for Climate Downscaling

Climate change is one of the most critical challenges that our planet is facing today. Rising global temperatures are already affecting Earth's weather and climate patterns with an increased frequency of unpredictable and extreme events. Future projections for climate change research are based on computer models like Earth System Models (ESMs). Climate simulations typically run on a coarser grid due to the high computational resources required, and then undergo a lighter downscaling process to obtain data on a finer grid. This work presents a self-supervised deep learning model that does not require high resolution ground truth data for downscaling. This is realized by leveraging salient distribution patterns and the hidden dependencies between weather variables for an individual data point at runtime. We propose three climate-specific components that well represent the patterns of underlying weather variables and learn intricate inter-variable dependencies. Extensive evaluation with 2x, 3x, and 4x scaling factors demonstrates that our model obtains 8% to 47% performance gain over existing baselines while greatly reducing the overall runtime. The improved performance and no dependence on high resolution ground truth data make our method a valuable tool for future climate research.