Machine learning models have been employed to perform either physics-free data-driven or hybrid dynamical downscaling of climate data. Most of these implementations operate over relatively small downscaling factors because of the challenge of recovering fine-scale information from coarse data. This limits their compatibility with many global climate model outputs, often available between $\sim$50--100 km resolution, to scales of interest such as cloud resolving or urban scales. This study systematically examines the capability of convolutional neural networks (CNNs) to downscale surface wind speed data over land surface from different coarse resolutions (25 km, 48 km, and 100 km resolution) to 3 km. For each downscaling factor, we consider three CNN configurations that generate super-resolved predictions of fine-scale wind speed, which take between 1 to 3 input fields: coarse wind speed, fine-scale topography, and diurnal cycle. In addition to fine-scale wind speeds, probability density function parameters are generated, through which sample wind speeds can be generated accounting for the intrinsic stochasticity of wind speed. For generalizability assessment, CNN models are tested on regions with different topography and climate that are unseen during training. The evaluation of super-resolved predictions focuses on subgrid-scale variability and the recovery of extremes. Models with coarse wind and fine topography as inputs exhibit the best performance compared with other model configurations, operating across the same downscaling factor. Our diurnal cycle encoding results in lower out-of-sample generalizability compared with other input configurations.

翻译：机器学习模型已被用于对气候数据进行无物理约束的数据驱动式或混合动力降尺度。由于从粗分辨率数据中恢复精细尺度信息存在挑战，这些实现大多在较小的降尺度因子下运行。这限制了它们与许多全球气候模型输出（其分辨率通常在约$\sim$50–100 km）在云解析或城市尺度等感兴趣尺度上的兼容性。本研究系统地考察了卷积神经网络（CNNs）将陆表风速数据从不同粗分辨率（25 km、48 km和100 km）降尺度至3 km的能力。对于每个降尺度因子，我们考虑三种CNN配置，它们生成超分辨率精细风速预测，并分别使用1到3个输入场：粗分辨率风速、精细尺度地形和日循环。除了精细风速外，还生成了概率密度函数参数，通过这些参数可生成考虑风速固有随机性的样本风速。为评估泛化能力，CNN模型在训练中未曾出现、且具有不同地形和气候的区域上进行测试。超分辨率预测的评估聚焦于子网格变异性及极值恢复。相较于其他在同一降尺度因子下运行的模型配置，以粗风速和精细地形为输入的模型性能最优。相比其他输入配置，我们的日循环编码导致样本外泛化能力较低。