用扩散模型提升降水图分辨率，精准还原极端降雨分布
Downscaling Precipitation with Bias-informed Conditional Diffusion Model
- 基于条件扩散模型学习高分辨率降水分布先验
- 8倍下采样精度超越传统确定性方法，有效捕捉极端降水
- 结合伽马校正与引导采样，显著降低偏差，适合气候风险评估
气候变化加剧了降雨极端事件，高分辨率降水预测对防洪等社会应对至关重要。然而当前全球气候模型（GCM）空间分辨率过低，难以支持局部分析。深度学习统计降尺度方法可在适度计算成本下提供高分辨率预测。本文提出一种偏置感知的条件扩散模型用于降水降尺度。模型利用条件扩散方法从大规模高分辨率降水数据中学习分布先验；针对降水分布的长尾特性，采用伽马校正进行预处理；为纠正降尺度结果中的偏差，引入引导采样策略增强偏差修正。实验表明，在8倍下采样条件下，该模型性能显著优于先前确定性方法，能更准确再现极端降雨特征。代码与数据集见 https://github.com/RoseLV/research_super-resolution。
原文摘要 · Abstract (English)
Climate change is intensifying rainfall extremes, making high-resolution precipitation projections crucial for society to better prepare for impacts such as flooding. However, current Global Climate Models (GCMs) operate at spatial resolutions too coarse for localized analyses. To address this limitation, deep learning-based statistical downscaling methods offer promising solutions, providing high-resolution precipitation projections with a moderate computational cost. In this work, we introduce a bias-informed conditional diffusion model for statistical downscaling of precipitation. Specifically, our model leverages a conditional diffusion approach to learn distribution priors from large-scale, high-resolution precipitation datasets. The long-tail distribution of precipitation poses a unique challenge for training diffusion models; to address this, we apply gamma correction during preprocessing. Additionally, to correct biases in the downscaled results, we employ a guided-sampling strategy to enhance bias correction. Our experiments demonstrate that the proposed model achieves highly accurate results in an 8 times downscaling setting, outperforming previous deterministic methods. The code and dataset are available at https://github.com/RoseLV/research_super-resolution
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