To mitigate global warming, greenhouse gas sources need to be resolved at a high spatial resolution and monitored in time to ensure the reduction and ultimately elimination of the pollution source. However, the complexity of computation in resolving high-resolution wind fields left the simulations impractical to test different time lengths and model configurations. This study presents a preliminary development of a physics-informed super-resolution (SR) generative adversarial network (GAN) that super-resolves the three-dimensional (3D) low-resolution wind fields by upscaling x9 times. We develop a pixel-wise self-attention (PWA) module that learns 3D weather dynamics via a self-attention computation followed by a 2D convolution. We also employ a loss term that regularizes the self-attention map during pretraining, capturing the vertical convection process from input wind data. The new PWA SR-GAN shows the high-fidelity super-resolved 3D wind data, learns a wind structure at the high-frequency domain, and reduces the computational cost of a high-resolution wind simulation by x89.7 times.

翻译：为缓解全球变暖，需以高空间分辨率解析温室气体源，并对其进行时序监测以确保污染源减排并最终消除。然而，高分辨率风场模型的计算复杂性使得模拟不同时间跨度和模型配置在实践上难以实现。本研究初步提出一种基于物理信息的超分辨率生成对抗网络，通过9倍上采样实现三维低分辨率风场的超分辨率重建。我们开发了像素级自注意力模块，该模块通过自注意力计算与二维卷积操作学习三维天气动力学特征。同时引入损失项，在预训练过程中对自注意力图进行正则化，从而从输入风场数据中捕捉垂直对流过程。新型PWA SR-GAN能生成高保真超分辨率三维风场数据，在频域内学习风场结构，并将高分辨率风场模拟的计算成本降低至原来的1/89.7。