Terrain-Guided Flatten Memory Network for Deep Spatial Wind Downscaling

Terrain-Guided Flatten Memory Network for Deep Spatial Wind Downscaling

Blog Article

High-resolution wind analysis plays an essential role in pollutant dispersion and renewable energy utilization.This article focuses on spatial wind downscaling.Specifically, a novel terrain-guided flatten memory network (abbreviated as TIGAM) with axial similarity Dryer Anti-Wrinkle Balls constraint is proposed.TIGAM consists of three elaborately designed blocks, i.e.

, the similarity block, the reconstruction block, and the denoise block.To achieve long-spatial dependence, the similarity block interpolates low-resolution data to high resolution in an axial attention manner.Meanwhile, the reconstruction block aims to obtain a clearer Horse Rugs high-resolution representation in closed form.Taking both of the meteorological prior and network design principle into consideration, this article also proposes a flatten memory module with learnable input for high-resolution denoising.Furthermore, for accurate detail reconstruction, a terrain-guided enhanced loss is presented benefitting from the high-resolution remote sensing data.

This loss function integrates wind spatial distribution and terrain elegantly.Extensive quantitative and qualitative experiments demonstrate the superiority of the proposed TIGAM.