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Coupling Downscalin...
Coupling Downscaling and Calibrating Methods for Generating High-Quality Precipitation Data with Multisource Satellite Data in the Yellow River Basin
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- Yang, Haibo (author)
- Zhengzhou University
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- Cui, Xiang (author)
- Zhengzhou University
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- Cai, Yingchun (author)
- Zhengzhou University
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- Wu, Zhengrong (author)
- Zhengzhou University
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- Gao, Shiqi (author)
- Zhengzhou University
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Yu, Bo (author)
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Wang, Yanling (author)
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Li, Ke (author)
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- Duan, Zheng (author)
- Lund University,Lunds universitet,BECC: Biodiversity and Ecosystem services in a Changing Climate,Centrum för miljö- och klimatvetenskap (CEC),Naturvetenskapliga fakulteten,MERGE: ModElling the Regional and Global Earth system,Institutionen för naturgeografi och ekosystemvetenskap,Centre for Environmental and Climate Science (CEC),Faculty of Science,Dept of Physical Geography and Ecosystem Science
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- Liang, Qiuhua (author)
- Loughborough University
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(creator_code:org_t)
- 2024
- 2024
- English.
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In: Remote Sensing. - 2072-4292. ; 16:8
- Related links:
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http://dx.doi.org/10... (free)
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https://lup.lub.lu.s...
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https://doi.org/10.3...
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Abstract
Subject headings
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- Remote sensing precipitation data have the characteristics of wide coverage and revealing spatiotemporal information, but their spatial resolution is low. The accuracy of the data is obviously different in different study areas and hydrometeorological conditions. This study evaluated four precipitation products in the Yellow River basin from 2001 to 2019, constructed the optimal combined product, conducted downscaling with various machine algorithms, and performed corrections using meteorological station precipitation data to analyze the spatiotemporal trends of precipitation. The results showed that (1) GPM and MSWEP had the best four evaluation indicators, with R2 values of 0.93 and 0.90, respectively, and the smallest FSE and RMSE, with a BIAS close to 0. A high-precision mixed precipitation dataset, GPM-MSWEP, was constructed. (2) Among the three methods, the downscaling results of DFNN showed higher accuracy. (3) The results, after correction with GWR, could more effectively enhance the accuracy of the data. (4) Precipitation in the Yellow River Basin showed a decreasing trend in January, September, and December, while it exhibited an increasing trend in other months and seasons, with 2002 and 2016 being points of abrupt change. This study provides a reference for the production of high-precision satellite precipitation products in the Yellow River basin.
Subject headings
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Meteorologi och atmosfärforskning (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Meteorology and Atmospheric Sciences (hsv//eng)
Keyword
- bias calibration
- downscaling
- machine learning
- satellite precipitation
- spatial variation characteristics
Publication and Content Type
- art (subject category)
- ref (subject category)
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- By the author/editor
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Yang, Haibo
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Cui, Xiang
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Cai, Yingchun
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Wu, Zhengrong
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Gao, Shiqi
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Yu, Bo
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show more...
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Wang, Yanling
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Li, Ke
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Duan, Zheng
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Liang, Qiuhua
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- About the subject
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- NATURAL SCIENCES
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NATURAL SCIENCES
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and Earth and Relate ...
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and Meteorology and ...
- Articles in the publication
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Remote Sensing
- By the university
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Lund University