| CLDASSD:Reconstructing Fine Textures of the Temperature Field Using Super-Resolution Technology |
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| 作者姓名： | Ruian TIE Chunxiang SHI Gang WAN Xingjie HU Lihua KANG Lingling GE |
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| 作者单位： | Institute of Aerospace Information;National Meteorological Information Center |
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| 基金项目： | the National Key Research and Development Program of China(Grant No.2018YFC1506604);the National Natural Science Foundation of China(Grant No.91437220)。 |
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| 摘 要： | Before 2008,the number of surface observation stations in China was small.Thus,the surface observation data were too sparse to effectively support the High-resolution China Meteorological Administration’s Land Assimilation System(HRCLDAS)which ultimately inhibited the output of high-resolution and high-quality gridded products.This paper proposes a statistical downscaling model based on a deep learning algorithm in super-resolution to research the above problem.Specifically,we take temperature as an example.The model is used to downscale the 0.0625°×0.0625°,2-m temperature data from the China Meteorological Administration’s Land Data Assimilation System(CLDAS)to 0.01°×0.01°,named CLDASSD.We performed quality control on the paired data from CLDAS and HRCLDAS,using data from 2018 and 2019.CLDASSD was trained on the data from 31 March 2018 to 28 February 2019,and then tested with the remaining data.Finally,extensive experiments were conducted in the Beijing-Tianjin-Hebei region which features complex and diverse geomorphology.Taking the HRCLDAS product and surface observation data as the"true values"and comparing them with the results of bilinear interpolation,especially in complex terrain such as mountains,the root mean square error(RMSE)of the CLDASSD output can be reduced by approximately 0.1℃,and its structural similarity(SSIM)was approximately 0.2 higher.CLDASSD can estimate detailed textures,in terms of spatial distribution,with greater accuracy than bilinear interpolation and other sub-models and can perform the expected downscaling tasks.
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| 关 键 词： | statistical downscaling deep learning temperature field high-resolution reconstruction |
CLDASSD: Reconstructing Fine Textures of the Temperature Field Using Super-Resolution Technology |
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| Ruian TIE,Chunxiang SHI,Gang WAN,Xingjie HU,Lihua KANG,Lingling GE.CLDASSD:Reconstructing Fine Textures of the Temperature Field Using Super-Resolution Technology[J].Advances in Atmospheric Sciences,2022,39(1):117-130. |
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| Authors: | Ruian TIE Chunxiang SHI Gang WAN Xingjie HU Lihua KANG Lingling GE |
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| Abstract: | Before 2008, the number of surface observation stations in China was small. Thus, the surface observation data were too sparse to effectively support the High-resolution China Meteorological Administration's Land Assimilation System (HRCLDAS) which ultimately inhibited the output of high-resolution and high-quality gridded products. This paper proposes a statistical downscaling model based on a deep learning algorithm in super-resolution to research the above problem. Specifically, we take temperature as an example. The model is used to downscale the 0.0625° × 0.0625°, 2-m temperature data from the China Meteorological Administration's Land Data Assimilation System (CLDAS) to 0.01° × 0.01°, named CLDASSD. We performed quality control on the paired data from CLDAS and HRCLDAS, using data from 2018 and 2019. CLDASSD was trained on the data from 31 March 2018 to 28 February 2019, and then tested with the remaining data. Finally, extensive experiments were conducted in the Beijing-Tianjin-Hebei region which features complex and diverse geomorphology. Taking the HRCLDAS product and surface observation data as the “true values” and comparing them with the results of bilinear interpolation, especially in complex terrain such as mountains, the root mean square error (RMSE) of the CLDASSD output can be reduced by approximately 0.1°C, and its structural similarity (SSIM) was approximately 0.2 higher. CLDASSD can estimate detailed textures, in terms of spatial distribution, with greater accuracy than bilinear interpolation and other sub-models and can perform the expected downscaling tasks. |
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| Keywords: | statistical downscaling deep learning temperature field high-resolution reconstruction |
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