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To mitigate global warming, greenhouse gas sources need to be resolved at ahigh spatial resolution and monitored in time to ensure the reduction andultimately elimination of the pollution source. However, the complexity ofcomputation in resolving high-resolution wind fields left the simulationsimpractical to test different time lengths and model configurations. This studypresents 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-wiseself-attention (PWA) module that learns 3D weather dynamics via aself-attention computation followed by a 2D convolution. We also employ a lossterm that regularizes the self-attention map during pretraining, capturing thevertical convection process from input wind data. The new PWA SR-GAN shows thehigh-fidelity super-resolved 3D wind data, learns a wind structure at thehigh-frequency domain, and reduces the computational cost of a high-resolutionwind simulation by x89.7 times.

A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network