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A deep learning-based imputation method for missing gaps in satellite aerosol products by fusing numerical model data

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成果类型:
期刊论文
作者:
Ning Liu;Yi Li*;Zengliang Zang;Yiwen Hu;Xin Fang;...
通讯作者:
Yi Li
作者机构:
[Ning Liu; Yi Li; Yiwen Hu] College of Meteorology and Oceanography, National University of Defense Technology, Changsha, 410003, China
High Impact Weather Key Laboratory of CMA, Changsha, 410003, China
[Xin Fang] School of Municipal and Geomatics Engineering, Hunan City University, Yiyang, 413000, China
National Research Council of Italy, Institute of Methodologies for Environmental Analysis (IMAA), Contrada S. Loja snc, 85050, Tito Scalo, PZ, Italy
CommSensLab, Dept. of Signal Theory and Communications, UPC, 08034, Barcelona, Spain
通讯机构:
[Yi Li] C
College of Meteorology and Oceanography, National University of Defense Technology, Changsha, 410003, China
语种:
英文
期刊:
Atmospheric Environment
ISSN:
1352-2310
年:
2024
页码:
120440
基金类别:
CRediT authorship contribution statement Ning Liu: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology. Yi Li: Writing – review & editing, acquisition. Zengliang Zang: Writing – review & editing, acquisition. Yiwen Hu: Writing – review & editing. Xin Fang: Writing – review & editing, acquisition. Simone Lolli: Writing – review & editing.
机构署名:
本校为其他机构
院系归属:
市政与测绘工程学院
摘要:
Satellite-based aerosol optical depth (AOD) products are commonly used in various aerosol-related studies, such as aerosol pollution mapping and aerosol-climate interactions. However, these satellite AOD products often suffer from significant missing gaps due to cloud cover and limitations in the retrieval algorithm. To address this issue, some studies take advantage of real-time seamless simulation of numerical models and successfully fill in these gaps by establishing a regression relationship between satellite AOD and numerical model AOD. Ho...

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