We analyze the applicability of convolutional neural network (CNN) architectures for downscaling of short-range forecasts of near-surface winds on extended spatial domains. Short-range wind field forecasts (at the 100 m level) from ECMWF ERA5 reanalysis initial conditions at 31 km horizontal resolution are downscaled to mimic HRES (deterministic) short-range forecasts at 9 km resolution. We evaluate the downscaling quality of four exemplary model architectures and compare these against a multi-linear regression model. We conduct a qualitative and quantitative comparison of model predictions and examine whether the predictive skill of CNNs can be enhanced by incorporating additional atmospheric variables, such as geopotential height and forecast surface roughness, or static high-resolution fields, like land-sea mask and topography. We further propose DeepRU, a novel U-Net-based CNN architecture, which is able to infer situation-dependent wind structures that cannot be reconstructed by other models. Inferring a target 9 km resolution wind field from the low-resolution input fields over the Alpine area takes less than 10 milliseconds on our GPU target architecture, which compares favorably to an overhead in simulation time of minutes or hours between low- and high-resolution forecast simulations.

中文翻译：

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用于风场降尺度的卷积神经网络模型的比较研究

我们分析了卷积神经网络 (CNN) 架构在扩展空间域上对近地表风的短期预测进行降尺度的适用性。来自 ECMWF ERA5 再分析初始条件在 31 公里水平分辨率下的短程风场预测（在 100 米级别）被缩小以模拟 9 公里分辨率的 HRES（确定性）短程预报。我们评估了四个示例模型架构的缩减质量，并将它们与多元线性回归模型进行了比较。我们对模型预测进行定性和定量比较，并检查是否可以通过结合额外的大气变量（例如位势高度和预测表面粗糙度）或静态高分辨率场（例如陆海掩膜和地形）来增强 CNN 的预测技能. 我们进一步提出了 DeepRU，这是一种新型的基于 U-Net 的 CNN 架构，它能够推断出其他模型无法重建的与情况相关的风结构。在我们的 GPU 目标架构上，从 Alpine 地区的低分辨率输入场推断目标 9 公里分辨率的风场只需不到 10 毫秒，这与低分辨率和高分辨率之间几分钟或几小时的模拟时间开销相比要有利预测模拟。