TC-SRNet: Reconstruction and prediction of typhoon high-resolution turbulent fields based on meteorological numerical forecast scale wind fields and deep learning method

Abstract

Meteorological numerical forecast models can provide a more accurate typhoon wind fields compared to engineering typhoon models due to their incorporate atmospheric multiphysical processes. However, the latest advancements in supercomputing power indicate that the current finest level of real-time weather forecasting typically operates on grid scales ranging from 1 to 4 km, while the convergence of typhoon intensity and turbulent field characteristics occurs at scales as fine as 62–185 m. Therefore, the primary scientific inquiry lies in determining how to achieve high-precision turbulent wind fields while considering the realistic atmospheric multiphysical processes, that means establish a “bridge” of typhoon wind field between kilometer-level and hundred-meter scales. This study investigates the super-resolution reconstruction of wind fields across different horizontal grid scales, utilizing a benchmark wind field at a 62 m horizontal grid scale (ground truth), which is based on a hybrid down-sampling skip connection (DSC)/multi-scale (MS) model. The research findings demonstrate that compared to traditional interpolation methods, the DSC/MS method significantly improves reconstruction accuracy, albeit with some residual high-frequency energy dissipation issues. Additionally, the DSC/MS method currently exhibits better reconstruction performance for 62 m scale wind fields based on kilometer-scale and smaller horizontal grid scales (1 km, 555 m, 185 m), with improved reconstruction as grid scale decreases. However, significant errors are observed in reconstructing fine turbulent fields at 62 m scale based on wind fields at 1.67 km horizontal grid scale. The findings presented in the present study can provide real and high-precision turbulent wind fields for structural wind engineering and wind energy assessment studies, thereby holding significant scientific and engineering application value.