基于扩散的亚得里亚海海洋场超分辨率
Diffusion-Based Super-Resolution of Adriatic Sea Oceanographic Fields
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中文总结 AI 辅助
提出OcDiffSR扩散模型，利用条件U-Net和多尺度编码器从粗分辨率再分析数据重建亚得里亚海高分辨率海洋场，在标量场误差和相关性上优于基线，实现高保真降尺度。
中文摘要 AI 辅助
高分辨率海洋场对于解析中尺度和次中尺度海岸动力学至关重要，但其生成仍受限于计算成本和观测稀疏性。我们提出了OcDiffSR，一种用于海洋超分辨率的有条件去噪扩散概率模型（DDPM），可从粗分辨率再分析输入重建高分辨率海面场。该模型在十年（2011-2020年）的成对低分辨率（GLORYS12V1，1/12°）和高分辨率（地中海物理再分析，Med MFC，1/24°）数据上训练，并在亚得里亚海的独立测试年（2009年）上评估。OcDiffSR采用条件U-Net，并增强了多尺度低分辨率编码器、交叉注意力瓶颈层以及通过特征级线性调制（FiLM）的正弦季节嵌入，从而实现对海面温度（SST）、盐度（SSS）和水平速度分量的联合超分辨率，并生成视觉上连贯的环流模式。与双线性插值和最先进的残差扩散模型CorrDiff相比，OcDiffSR在标量场上实现了显著更低的重建误差（RMSESST=0.477°C，RMSESSS=0.346 psu），近乎完美的皮尔逊相关性（PCC≥0.999）和高结构相似性（SSIM≥0.964）。对于动力矢量场，OcDiffSR在绝对误差和空间连贯性方面均优于两个基线，但中等相关性（PCC=0.64）反映了海洋速度场固有的随机性。日度和月度评估确认了所有季节的时间鲁棒性。这些结果确立了OcDiffSR作为高保真海洋降尺度和再分析增强的可靠框架，生成的场与已知海洋动力学视觉上一致。
英文摘要
High-resolution oceanographic fields are critical for resolving mesoscale and sub-mesoscale coastal dynamics, yet their generation remains constrained by both computational cost and observational sparsity. We present OcDiffSR, a conditional denoising diffusion probabilistic model (DDPM) for oceanographic super-resolution that reconstructs high-resolution sea-surface fields from coarse-resolution reanalysis inputs. The model is trained on ten years (2011-2020) of paired low-resolution (GLORYS12V1, 1/12) and high-resolution (Mediterranean Sea Physics Reanalysis, Med MFC, 1/24) data, and evaluated on an independent test year (2009) over the Adriatic Sea. OcDiffSR employs a conditional U-Net augmented with multi-scale low-resolution encoders, cross-attention bottleneck layers, and sinusoidal seasonal embeddings via Feature-wise Linear Modulation (FiLM), enabling joint super-resolution of sea-surface temperature (SST), salinity (SSS), and horizontal velocity components with visually coherent circulation patterns. Benchmarked against bilinear interpolation and the state-of-the-art residual diffusion model CorrDiff, OcDiffSR achieves substantially lower reconstruction errors for scalar fields (RMSESST=0.477 C, RMSESSS=0.346 psu), near-unity Pearson correlation (PCC >= 0.999), and high structural similarity (SSIM >= 0.964). For dynamical vector fields, OcDiffSR outperforms both baselines in absolute error and spatial coherence, though moderate correlation (PCC = 0.64) reflects the intrinsic stochasticity of oceanic velocity fields. Daily and monthly evaluations confirm temporal robustness across all seasons. These results establish OcDiffSR as a reliable framework for high-fidelity oceanographic downscaling and reanalysis enhancement, producing fields that are visually consistent with known ocean dynamics.
发表机构
- University of Salento（萨伦托大学）
机构由 AI 辅助整理，请以论文原文为准。
