Super-resolution reconstruction of wind fields with a swin-transformer-based deep learning framework

Deep learning approaches that allow for the rapid simulation of high-resolution atmospheric turbulence are expected by using the super-resolution (SR) technique. Recently, the shifted window attention mechanism in Swin-Transformer offers a significant improvement compared with the vanilla attention mechanism. This method restricts the attention computation to a local neighborhood, reducing the computational load to a linear relationship with sequence length. However, its original architecture is unsuitable for the SR in turbulence due to the mismatch with classification, detection, and segmentation tasks. In this study, the hierarchical structure is redesigned, and a new relative position representing approach is introduced to facilitate the SR procedures of turbulent wind. The channel-shuffled perceptual loss is integrated into the loss function to guide the update of weight parameters. The experimental cases of idealized two-dimensional turbulent flow and realistic boundary layer wind are employed to validate the performance. The results suggest that the proposed approach remarkably outperformed the previous Super-Resolution Convolutional Neural Network, Deep Statistical Downscaling, and Regional Climate Model Emulator in wind vectors. It exhibits lower values than the other three networks whether in terms of point-wise metrics like mean square error, mean absolute error, mean absolute percentage error, or perceptual metrics, including structural similarity index measure and probability density functions. The reconstructed wind vectors closely match the target high-resolution results. This study will help advance the application of shifted window attention mechanisms in wind field SR.