Super-resolution of near-surface temperature utilizing physical quantities for real-time prediction of urban micrometeorology
Super-resolution of near-surface temperature utilizing physical quantities for real-time prediction of urban micrometeorology
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DOI：
10.1016/j.buildenv.2021.108597
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发表时间：
2021-08
影响因子：
7.4
通讯作者：
Y. Yasuda;R. Onishi;Y. Hirokawa;D. Kolomenskiy;D. Sugiyama
中科院分区：
文献类型：
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作者：
Y. Yasuda;R. Onishi;Y. Hirokawa;D. Kolomenskiy;D. Sugiyama
提出了一种基于卷积神经网络的超分辨率(SR)模型，并将其应用于城市近地表温度的预测。SR模型集成了跳跃连接、通道注意机制，以及用于输入温度、建筑高度、向下短波辐射和水平速度的独立特征提取器。我们用一个城市建筑物分辨率大涡模拟(LESS)的低分辨率(LR)和高分辨率(HR)图像集训练SR模型，其中LR和HR的水平分辨率分别为20和5微米，证实了SR模型在另一个城市的推广能力。与双三次插值法和仅以温度为输入的图像SR模型相比，所估计的HR温度场具有更高的精度。除了温度输入，建筑物高度是重建HR温度的最重要因素，并且使SR模型能够减少建筑物边界附近的温度误差。SR模型根据建筑物的高度信息考虑每个建筑物的适当边界。关注权重分析表明，建筑物高度的重要性随着向下短波辐射的增大而增加。随着太阳辐射的增加，日照和遮荫的对比度增强，这可能会影响温度分布。较短的推断时间表明，通过将所提出的SR模型与LR建筑-分辨率LES模型相结合，可以促进大都市地区的实时人力资源预测。
The present paper proposes a super-resolution (SR) model based on a convolutional neural network and applies it to the near-surface temperature in urban areas. The SR model incorporates a skip connection, a channel attention mechanism, and separated feature extractors for the inputs of temperature, building height, downward shortwave radiation, and horizontal velocity. We train the SR model with sets of low-resolution (LR) and high-resolution (HR) images from building-resolving large-eddy simulations (LESs) in a city, where the horizontal resolutions of LR and HR are 20 and 5 m, respectively The generalization capability of the SR model is confirmed with LESs in another city. The estimated HR temperature fields are more accurate than those of the bicubic interpolation and image SR model that takes only the temperature as its input. Except for the temperature input, the building height is the most important to reconstruct the HR temperature and enables the SR model to reduce errors in temperature near building boundaries. The SR model considers the appropriate boundary for each building from its height information. The analysis of attention weights indicates that the importance of the building height increases as the downward shortwave radiation becomes larger. The contrast between sun and shade is strengthened with the increase in solar radiation, which may affect the temperature distribution. The short inference time suggests the potential of the proposed SR model to facilitate a real-time HR prediction in metropolitan areas by combining it with an LR building-resolving LES model.