Terrain-Guided Flatten Memory Network for Deep Spatial Wind Downscaling

Abstract

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 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 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.

Keywords

• Deep learning
• flatten memory network
• image super-resolution
• meteorological methods
• spatial wind downscaling