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• W4312963886 abstract "High-resolution wind analysis plays an essential role in pollutant dispersion and renewable energy utilization. This paper focuses on spatial wind downscaling. Specifically, a novel <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink/> <bold xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>t</b> <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink/> erra <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink/> <bold xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>i</b> <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink/> n <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink/> <bold xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>g</b> <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink/> uided fl <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink/> <bold xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>a</b> <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink/> tten <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink/> <bold xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>m</b> <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink/> emory 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 dependency, 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 paper 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." @default.
• W4312963886 created "2023-01-05" @default.
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• W4312963886 date "2022-01-01" @default.
• W4312963886 modified "2023-09-26" @default.
• W4312963886 title "Terrain-Guided Flatten Memory Network for Deep Spatial Wind Downscaling" @default.
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• W4312963886 doi "https://doi.org/10.1109/jstars.2022.3218016" @default.
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