Dataset List

Vol.|Area

Data Details

Centennial Homogenized Monthly/Yearly Mean Temperature Dataset at Jiujiang Meteorological Station, Jiangxi Province of China (1924-2023)


ZHAN Longfei1,2XU Bin*1,2DONG Baohua1,2LI Yong1,2
1 Jiangxi Provincial Climate Center,Nanchang 330096,China2 Nanchang National Climate Observatory,Nanchang 330200,China

DOI:10.3974/geodb.2024.12.03.V1

Published:Dec. 2024

Visitors:900       Data Files Downloaded:17      
Data Downloaded:0.13 MB      Citations:

Key Words:

Jiujiang,temperature,centennial series,interpolation,homogenization

Abstract:

Long-term homogenized observation series are essential for the accurate assessment and attribution of climate change. However, most of the meteorological stations have been affected by unnatural factors such as station relocation, instrument change, and environmental changes, resulting in non-homogenization in the observation series. Based on the monthly mean temperature data from multiple sources, combined with data integration and quality control, we used the standardized series method to interpolate the month-to-month mean temperature data from Jiangxi Jiujiang Meteorological Station for the period of 1924-2023. The homogeneity of the interpolated data was examined by using the penalized maximum F-test (PMF) method, and at the same time, it was revised by using the quartile matching (QM) method, and the centennial mean temperature data from Jiangxi Jiujiang Station was constructed by using the standardized series method. The centennial homogenized monthly/yearly mean temperature dataset at Jiujiang station was obtained. The comparison analysis with the homogenized 100-year mean air temperature data of the neighboring meteorological stations shows that the correlation between the 100-year monthly/yearly mean air temperature dataset of Jiujiang Meteorological Station constructed in this paper and the neighboring stations reaches more than 0.9, which verifies the scientific validity of the construction method and the reliability of this dataset. This dataset is the monthly/yearly mean air temperature (unit ℃) of Jiujiang meteorological station from 1924 to 2023. The dataset is archived in .txt data format, and consists of two data files with data size of 16.5 KB.

Foundation Item:

China Meteorological Administration (CMA2024QN15); Science and Technology Department of Jiangxi Province (20223BBG71019, 2023KYG01001); Shanghai Meteorological Service (QYHZ202106); Guangdong Meteorological Service (ZJLY202312); Nanchang National Climatic Observatory (JX2023Z09); Jiangxi Meteorological Service (JX2022ZHHFXPC06)

Data Citation:

ZHAN Longfei, XU Bin*, DONG Baohua, LI Yong. Centennial Homogenized Monthly/Yearly Mean Temperature Dataset at Jiujiang Meteorological Station, Jiangxi Province of China (1924-2023)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2024. https://doi.org/10.3974/geodb.2024.12.03.V1.

References:


     [1] Arnella, N. W., Lowe, J. A., Challinor, A. J., et al. Global and regional impacts of climate change at different levels of global temperature increase [J]. Climatic Change, 2019, 155(3): 377-391.
     [2] Pan, X. D. A review of future climate change research based on regional climate models [J]. Plateau Meteorology, 2018, 37(5): 1440-1448.
     [3] Ren, G., Chan, J. C. L., Kubota, H., et al. Historical and recent change in extreme climate over East Asia [J]. Climatic Change, 2021, 168(3): 1-19.
     [4] Zhang, J., Liu, B., Ren, S., et al. A 4 km daily gridded meteorological dataset for China from 2000 to 2020 [J]. Scientific Data, 2024, 11(1): 1230.
     [5] Tang, G. L., Ding, Y. H., Wang, S. W., et al. Comparative analysis of temperature curves in China over the past century [J]. Advances in Climate Change Research, 2009, 5(2): 71-78.
     [6] S, P., Hao, L. S., Luo, C. J., et al. Interpolation and homogeneity correction of missing temperature records at Baoding Meteorological Station in Hebei [J]. Advances in Climate Change Research, 2017, 13(1): 41-51.
     [7] Li, Q. X. Current status and prospects of homogeneity research in Chinese climate data [J]. Meteorological Science and Technology, 2016, 6(3): 67-74.
     [8] Shen, Y. L. Construction and evaluation of a high-resolution meteorological element grid dataset for Qinghai Province [J]. Meteorological Science and Technology, 2023, 13(4): 29-33.
     [9] Wang, H. J., Tu, S. Y., Chen, Z. H. Experiment and error analysis of interpolation methods for missing daily temperature data [J]. Meteorology, 2008, 34(7): 83-91.
     [10] Wang, Y. P. Comparison and homogeneity analysis of observation data from old and new meteorological stations in Gansu Province [J]. Plateau Meteorology, 2023, 42(2): 506-514.
     [11] Li, Z., Yan, Z., Cao, L., et al. Further-adjusted long-term temperature series in China based on MASH [J]. Advances in Atmospheric Sciences, 2018, 35(8): 909-917.
     [12] Yu, Y., Li, J., Ren, Z. H., et al. Application of standard series method in interpolation of missing daily average temperature data [J]. Meteorology, 2012, 38(9): 1135-1139.
     [13] Si, P., Xie, Y. Y. Homogeneity analysis of total solar radiation data in Tianjin [J]. Climate and Environment Research, 2015, 20(3): 269-276.
     [14] Wang, X. L., Chen, H., Wu, Y., et al. New techniques for the detection and adjustment of shifts in daily precipitation data series [J]. Journal of Applied Meteorology and Climatology, 2010, 49(12): 2416-2436.
     

Data Product:

ID Data Name Data Size Operation
1 AnnualMeanTempJiujiang1924-2023.txt 1.23KB
2 MonthlyMeanTempJiujiang1924-2023.txt 15.34KB
Co-Sponsors
Superintend