Analysis of revising multisource fusion data of high-temperature flood season weather in southern Xinjiang, China

To obtain a more accurate temperature distribution in areas with complex terrain, we analysed the hourly temperature product of the Land Surface Data Assimilation System of the China Meteorological Administration (CLDAS) from June to August 2022, with a spatial resolution of 0.05° × 0.05°. An innova...

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Veröffentlicht in:Theoretical and applied climatology Jg. 155; H. 7; S. 5795 - 5806
Hauptverfasser: Zhang, Zulian, Wang, Mingquan, Meng, Fanxue, Gu, Yawen, Aidaituli, Mushajiang, Jiang, Yuanan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Vienna Springer Vienna 01.07.2024
Springer
Schlagworte:
Algorithms
Aquatic Pollution
Atmospheric Protection/Air Quality Control/Air Pollution
Atmospheric Sciences
China
Climatology
Comparative analysis
Earth and Environmental Science
Earth Sciences
Floods
Geospatial data
landscapes
spatial data
temperature
time series analysis
Waste Water Technology
Water Management
Water Pollution Control
weather
China
ISSN:0177-798X, 1434-4483
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Zusammenfassung:To obtain a more accurate temperature distribution in areas with complex terrain, we analysed the hourly temperature product of the Land Surface Data Assimilation System of the China Meteorological Administration (CLDAS) from June to August 2022, with a spatial resolution of 0.05° × 0.05°. An innovative stepwise proximity error correction algorithm was proposed based on the distribution of automatic weather stations in southern Xinjiang. CLDAS and revised CLDAS data were obtained and analysed from time series and spatial data series, respectively. In comparative analysis, the following were considered: the root mean square error, the temperature accuracy at 1 and 2 °C, the high-temperature accuracy at 35, 37 and 40 °C, and the distribution of high temperatures. The error variation trends of the two types of multisource fusion data were determined. Our analysis proved that the use of the correction algorithm could effectively improve the accuracy and adaptability of CLDAS data in southern Xinjiang. The proposed algorithm could provide detailed locations and more accurate temperature values for high-temperature monitoring and could serve as a reference for relevant studies.
Bibliographie:ObjectType-Article-1
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ISSN:0177-798X
1434-4483
DOI:10.1007/s00704-024-04964-0