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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Vydáno v:	Theoretical and applied climatology Ročník 155; číslo 7; s. 5795 - 5806
Hlavní autoři:	Zhang, Zulian, Wang, Mingquan, Meng, Fanxue, Gu, Yawen, Aidaituli, Mushajiang, Jiang, Yuanan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Vienna Springer Vienna 01.07.2024 Springer
Témata:	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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Abstract	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.
AbstractList	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. 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. 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° x 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.
Audience	Academic
Author	Zhang, Zulian Jiang, Yuanan Aidaituli, Mushajiang Meng, Fanxue Gu, Yawen Wang, Mingquan
Author_xml	– sequence: 1 givenname: Zulian surname: Zhang fullname: Zhang, Zulian organization: Xinjiang Uygur Autonomous Region Meteorological Observatory, College of Geography and Remote Sensing Sciences, XinJiang University, Xinjiang Xingnong Net Information Center – sequence: 2 givenname: Mingquan surname: Wang fullname: Wang, Mingquan email: mingquanwang2023@hotmail.com organization: Xinjiang Education Management Information Center – sequence: 3 givenname: Fanxue surname: Meng fullname: Meng, Fanxue organization: Kashgar Meteorological Bureau, Xinjiang Uygur Autonomous Region – sequence: 4 givenname: Yawen surname: Gu fullname: Gu, Yawen organization: Xinjiang Xingnong Net Information Center – sequence: 5 givenname: Mushajiang surname: Aidaituli fullname: Aidaituli, Mushajiang organization: Xinjiang Xingnong Net Information Center – sequence: 6 givenname: Yuanan surname: Jiang fullname: Jiang, Yuanan organization: Xinjiang Uygur Autonomous Region Meteorological Observatory
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Accessed 1 Mar 2024 – reference: LiuYShiCXWangHJHanSApplicability assessment of CLDAS temperature data in ChinaTrans Atmos Sci202144454054810.13878/j.cnki.dqkxxb.20200819001 – reference: SadroSSickmanJOMelackJMSkeenKEffects of climate variability on snowmelt and implications for organic matter in a high-elevation lakeWater Resour Res201854456345781:CAS:528:DC%2BC1cXhsFGru7nO10.1029/2017WR022163 – reference: YangFYPengFYuFEvaluation of applicability and correction for the CLDAS temperature and relative humidity products in Guizhou ProvincePlateau Meteorol202342247248210.7522/j.issn.1000-0534.2021.00064 – reference: Wang H, Wang MX, Wang SL, Yu XJ (2021) Spatial-temporal variation characteristics of snow cover duration in Xinjiang from 1961 to 2017 and their relationship with meteorological factors. 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SubjectTerms	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
Title	Analysis of revising multisource fusion data of high-temperature flood season weather in southern Xinjiang, China
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