  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.
