用克里金信息增强扩散模型，提升海平面数据降尺度精度
Towards Kriging-informed Conditional Diffusion for Regional Sea-Level Data Downscaling
- 融合克里金插值先验的条件扩散模型，建模精细空间依赖
- 在真实海平面上升数据上优于现有最先进方法
- 适合需要高精度区域气候预测的研究者
基于全球气候模型或卫星数据的粗分辨率投影，降尺度问题旨在估计更高分辨率的区域气候数据，捕捉细尺度空间模式与变异性。降尺度是通过低分辨率变量推导高分辨率数据的方法，常用于提供更详细、局部化的预测与分析。该问题对有效应对气候变化带来的重大风险具有重要社会意义。挑战在于空间异质性以及恢复细尺度特征的同时保证模型泛化能力。大多数现有降尺度方法无法捕捉细尺度的空间依赖，在实际气候数据集（如海平面上升数据）上表现不佳。本文提出一种新型的克里金信息引导的条件扩散概率模型（Ki-CDPM），在保留细尺度特征的同时有效捕捉空间变异性。在气候数据上的实验结果表明，所提方法比现有最先进技术更具准确性。
原文摘要 · Abstract (English)
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.
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