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首页> 外文期刊>SOLA: Scientific Online Letters on the Atmosphere >Super-Resolution Simulation for Real-Time Prediction of Urban Micrometeorology
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Super-Resolution Simulation for Real-Time Prediction of Urban Micrometeorology

机译：城市微观物理实时预测的超分辨率模拟

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摘要

We propose a super-resolution (SR) simulation system that consists of a physics-based meteorological simulation and an SR method based on a deep convolutional neural network (CNN). The CNN is trained using pairs of high-resolution (HR) and low-resolution (LR) images created from meteorological simulation results for different resolutions so that it can map LR simulation images to HR ones. The proposed SR simulation system, which performs LR simulations, can provide HR prediction results in much shorter operating cycles than those required for corresponding HR simulation prediction system. We apply the SR simulation system to urban micrometeorology, which is strongly affected by buildings and human activity. Urban micrometeorology simulations that need to resolve urban buildings are computationally costly and thus cannot be used for operational real-time predictions even when run on supercomputers. We performed HR micrometeorology simulations on a supercomputer to obtain datasets for training the CNN in the SR method. It is shown that the proposed SR method can be used with a spatial scaling factor of 4 and that it outperforms conventional interpolation methods by a large margin. It is also shown that the proposed SR simulation system has the potential to be used for operational urban micrometeorology predictions.
机译：我们提出了一种超分辨率（SR）模拟系统，该系统包括基于物理的气象模拟和基于深卷积神经网络（CNN）的SR方法。 CNN使用从多数分辨率的高分辨率（HR）和低分辨率（LR）图像进行培训，从而为不同的分辨率造成的气象仿真结果，使其可以将LR模拟图像映射到HR。所提出的SR模拟系统执行LR模拟，可以提供高于操作周期的HR预测，而不是相应的HR仿真预测系统所需的工作循环。我们将SR仿真系统应用于城市微观物理学，受建筑物和人类活动的强烈影响。需要解决城市建筑的城市微观气象模拟是计算的昂贵，因此即使在超级计算机上运行时也不能用于运营实时预测。我们对超级计算机进行了HR MicroMetorology模拟，以获得用于在SR方法中训练CNN的数据集。结果表明，所提出的SR方法可以与空间缩放因子为4，并且它通过大边距优于传统的插值方法。还表明，所提出的SR仿真系统具有用于运营城市微观定理预测的可能性。

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