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

• Published in: Theoretical and applied climatology Vol. 155; no. 7; pp. 5795 - 5806
• Main Authors: Zhang, Zulian, Wang, Mingquan, Meng, Fanxue, Gu, Yawen, Aidaituli, Mushajiang, Jiang, Yuanan
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.07.2024
• Subjects: Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Atmospheric Sciences; Climatology; Earth and Environmental Science; Earth Sciences; Waste Water Technology; Water Management; Water Pollution Control
• ISSN: 0177-798X; 1434-4483
• DOI: 10.1007/s00704-024-04964-0

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.