近地表风的算法幻觉：利用生成对抗网络进行统计降尺度至对流允许尺度
Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales
AI总结：
本文研究利用生成对抗网络将低分辨率气象数据统计降尺度至对流允许尺度，提出部分频率分离方案以改善生成风场的空间结构，并揭示了内部变率及目标函数对降尺度性能的影响。
摘要：
本文探讨了将图像超分辨率（SR）领域的新兴机器学习方法应用于统计降尺度任务。我们特别关注基于卷积神经网络的生成对抗网络（GANs）。我们的GANs以低分辨率（LR）输入为条件，生成模拟北美地区天气研究与预报（WRF）模型模拟的高分辨率（HR）地表风场。与传统的SR模型不同——在传统SR模型中，LR输入是HR图像的理想化粗化版本——WRF模拟涉及使用非理想化的LR和HR配对，由于内部变率导致共享尺度上的不匹配。我们的研究建立在当前基于SR的统计降尺度工作之上，通过实验引入了一种来自计算机视觉领域的新型频率分离（FS）方法。为了评估SR模型的技巧，我们精心选择了评估指标，并重点关注基于空间功率谱的性能度量。我们的分析揭示了GAN配置如何影响生成场中的空间结构，特别是空间变率谱中的偏差。利用功率谱评估FS实验表明，FS在计算机视觉中的成功应用并不能直接迁移到气候场。然而，FS实验证明了功率谱对一种常用的基于GAN的SR目标函数的敏感性，这有助于解释和理解其在决定空间结构中的作用。这一结果推动了新型部分频率分离方案的开发，将其作为一种有前景的配置选项。我们还量化了由内部变率引起的非理想化LR场对GAN性能的影响。此外，我们进行了一项基于谱的特征重要性实验，使我们能够探索生成场的空间结构对不同物理相关LR协变量的依赖性。
英文摘要：
This paper explores the application of emerging machine learning methods from image super-resolution (SR) to the task of statistical downscaling. We specifically focus on convolutional neural network-based Generative Adversarial Networks (GANs). Our GANs are conditioned on low-resolution (LR) inputs to generate high-resolution (HR) surface winds emulating Weather Research and Forecasting (WRF) model simulations over North America. Unlike traditional SR models, where LR inputs are idealized coarsened versions of the HR images, WRF emulation involves using non-idealized LR and HR pairs resulting in shared-scale mismatches due to internal variability. Our study builds upon current SR-based statistical downscaling by experimenting with a novel frequency-separation (FS) approach from the computer vision field. To assess the skill of SR models, we carefully select evaluation metrics, and focus on performance measures based on spatial power spectra. Our analyses reveal how GAN configurations influence spatial structures in the generated fields, particularly biases in spatial variability spectra. Using power spectra to evaluate the FS experiments reveals that successful applications of FS in computer vision do not translate to climate fields. However, the FS experiments demonstrate the sensitivity of power spectra to a commonly used GAN-based SR objective function, which helps interpret and understand its role in determining spatial structures. This result motivates the development of a novel partial frequency-separation scheme as a promising configuration option. We also quantify the influence on GAN performance of non-idealized LR fields resulting from internal variability. Furthermore, we conduct a spectra-based feature-importance experiment allowing us to explore the dependence of the spatial structure of generated fields on different physically relevant LR covariates.
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