Towards Kriging-informed Conditional Diffusion for Regional Sea-Level Data Downscaling
This addresses the need for detailed local climate predictions for adaptation and resilience, though it appears incremental as it builds on existing diffusion models with a kriging enhancement.
The paper tackled the problem of downscaling coarse-resolution regional sea-level data to finer resolutions, proposing a Kriging-informed Conditional Diffusion Probabilistic Model (Ki-CDPM) that achieved higher accuracy than state-of-the-art methods.
Given coarser-resolution projections from global climate models or satellite data, the downscaling problem aims to estimate finer-resolution regional climate data, capturing fine-scale spatial patterns and variability. Downscaling is any method to derive high-resolution data from low-resolution variables, often to provide more detailed and local predictions and analyses. This problem is societally crucial for effective adaptation, mitigation, and resilience against significant risks from climate change. The challenge arises from spatial heterogeneity and the need to recover finer-scale features while ensuring model generalization. Most downscaling methods \cite{Li2020} fail to capture the spatial dependencies at finer scales and underperform on real-world climate datasets, such as sea-level rise. We propose a novel Kriging-informed Conditional Diffusion Probabilistic Model (Ki-CDPM) to capture spatial variability while preserving fine-scale features. Experimental results on climate data show that our proposed method is more accurate than state-of-the-art downscaling techniques.