通过域对齐生成无监督降尺度：应用于风场
Generative Unsupervised Downscaling of Climate Models via Domain Alignment: Application to Wind Fields
AI总结：
本文提出基于域对齐的生成模型，用于多变量风场降尺度与偏差校正，提升空间一致性与鲁棒性。
摘要：
通用环流模型（GCMs）广泛用于未来气候预测，但其粗略的空间分辨率和系统性偏差限制了其在影响研究中的直接应用。此限制在风相关应用中尤为关键，如风能，需要空间一致、多变量且物理上合理的近地面风场。经典统计降尺度和偏差校正方法部分解决了这一问题，但难以保持空间结构、变量间一致性及在气候变化下的鲁棒性，特别是在高维情况下。近期生成机器学习的进步为降尺度和偏差校正提供了新机会，消除了显式配对低/高分辨率数据集的需要。然而，许多现有方法仍难以解释且难以在运营气候影响研究中部署。本文应用SerpentFlow，一种可解释、生成、域对齐框架，用于从GCM输出中进行多变量降尺度和偏差校正。该方法通过分离大尺度空间模式与小尺度变异性生成低/高分辨率训练数据对。大尺度成分在气候模型和观测域中对齐。然后使用流匹配生成模型学习条件细尺度变异性。该方法应用于多个风变量降尺度，包括平均和最大风速、东西向和南北向分量，并与广泛应用的多变量偏差校正方法进行比较。结果表明在空间一致性、变量间一致性和未来气候条件下的鲁棒性方面有所提升，突显了可解释生成模型在风能和能源应用中的潜力。
英文摘要：
General Circulation Models (GCMs) are widely used for future climate projections, but their coarse spatial resolution and systematic biases limit their direct use for impact studies. This limitation is particularly critical for wind-related applications, such as wind energy, which require spatially coherent, multivariate, and physically plausible near-surface wind fields. Classical statistical downscaling and bias correction methods partly address this issue. Still, they struggle to preserve spatial structure, inter-variable consistency, and robustness under climate change, especially in high-dimensional settings. Recent advances in generative machine learning offer new opportunities for downscaling and bias correction, eliminating the need for explicitly paired low- and high-resolution datasets. However, many existing approaches remain difficult to interpret and challenging to deploy in operational climate impact studies. In this work, we apply SerpentFlow, an interpretable, generative, domain alignment framework, to the multivariate downscaling and bias correction of wind variables from GCM outputs. This is a method that generates low-resolution/high-resolution training data pairs by separating large-scale spatial patterns from small-scale variability. Large-scale components are aligned across climate model and observational domains. Conditional fine-scale variability is then learned using a flow-matching generative model. We apply the approach to multiple wind variables downscaling, including average and maximal wind speed, zonal and meridional components, and compare it with widely used multivariate bias correction methods. Results show improved spatial coherence, inter-variable consistency, and robustness under future climate conditions, highlighting the potential of interpretable generative models for wind and energy applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
