Yearly Dataset of the Atmospheric Water Substance Cycle and Cloud Water Resource in the Mainland of China (2000-2023)
ZHOU Yuquan1CAI Miao1OU Jianjun2YU Jie3LIU Jianzhao4TANG Yahui1
1 CMA Key Laboratory for Cloud Physics，Weather Modification Center，China Meteorological Administration (CMA)，Beijing 100081，China2 Shanghai By Weather Technology Company，Shanghai 201306，China3 Chengdu University of Information Technology，Chengdu 610225，China4 Northeast Institute of Geography and Agroecology，Changchun 130102，China
DOI:10.3974/geodb.2025.03.03.V1
Published:Mar. 2025
Visitors：487 Data Files Downloaded：14
Data Downloaded：11.40 MB Citations：
Key Words：
Cloud water resource, Hydrometeor circulation, Atmospheric hydrometeor, Precipitation efficiency, Diagnostic evaluation
Abstract：
The yearly dataset of the atmospheric water substance cycle and cloud water resource in the Mainland of China (2000-2023) was developed based on cloud observations from the CLOUDSAT/CALIPSO satellites, atmospheric reanalysis data from NCEP/NCAR, and the Global Precipitation Climate Project (GPCP) dataset, combined with aircraft cloud physics measurements. Cloud Water Resource Diagnostic Evaluation System (CWR-NQ) was utilized to construct a 1° x 1° grid of cloud water, water vapor, and precipitation fields for the period from 2000 to 2023. The boundaries of the mainland of China and the six planning zones were used to obtain the assessments for atmospheric water cycles and cloud water resource for different regions and time periods. The dataset includes: (1) physical results of atmospheric water cycle and cloud water resources in the mainland of China from 2000 to 2023, which consists of 29 physical quantities: state variables, fluxes, and source-sink quantities related to the atmospheric water cycle, the total amount of atmospheric water, conversion efficiency, and renewal period, as well as atmospheric and cloud water resource quantities; (2) the boundary data of the six planning zones (Cited data). The dataset is archived in the .xlsx and .shp data formats, and consists of 9 data files with data size of 1.08 MB (Compressed into one file with 833 KB). The analysis papers based on the dataset were published at Journal of Meteorological Research (Vol. 36, No. 2, 2022) and Acta Meterologica Sinica (Vol. 82, No. 4, 2024).
Foundation Item：
Ministry of Science and Technology of P. R. China (2016YFA0601701) ；
Data Citation：
ZHOU Yuquan, CAI Miao, OU Jianjun, YU Jie, LIU Jianzhao, TANG Yahui. Yearly Dataset of the Atmospheric Water Substance Cycle and Cloud Water Resource in the Mainland of China (2000-2023)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2025. https://doi.org/10.3974/geodb.2025.03.03.V1.
Related Publication：
[1] Cai, M., Zhou, Y. Q. Liu, J. Z. et al., Diagnostic quantification of the cloud water resource in China during 2000-2019 [J]. Journal of Meteorological Research, 2022, 36(2): 292-310.
[2] Yu, J., Cai, M., Zhou, Y. Q., et al. Spatiotemporal characteristics of cloud water resources in Northwest China from 2000 to 2019 [J]. Acta Meterologica Sinica, 2024, 82(4): 476-489.
References：
[1] Huffman, G. J., Adler, R. F., Morrissey, M. M. et al., Global precipitation at one-degree daily resolution from multi satellite observations [J]. Journal of Hydrometeorology, 2001, 2(1): 36-50.
[2] National Centers for Environmental Prediction/National Weather Service/NOAA, U.S. Department of Commerce. NCEP FNL operational model global tropospheric analyses, continuing from July 1999 [DB]. National Center for Atmospheric Research, Computational and Information Systems Laboratory. 2000.
[3] Zhou, Y. Q., Cai, M., Tan, C., et al., Quantifying the cloud water resource: Basic concepts and characteristics [J]. Journal of Meteorological Research, 2020, 34(6): 1242-1255.
[4] Cai, M., Y. Q. Zhou, J. Z. Liu, et al., Quantifying the cloud water resource: Methods based on observational diagnosis and cloud model simulation [J]. Journal of Meteorological Research, 2020, 34(6): 1256-1270.
[5] Cai, M., Zhou, Y. Q., Ou, J. J., et al. Study on diagnosing three dimensional cloud region [J]. Plateau Meteorology, 2015, 34(5): 1330-1344.
[6] Cai, M., Ou, J. J., Zhou Y. Q., et al. Discriminating cloud area by using L-Band sounding data [J]. Chinese Journal of Atmospheric Sciences, 2014, 38(2): 213-222.
[7] National Development and Reform Commission, China Meteorological Administration. National Weather Modification Development Plan (2014-2020) [OL]. https://www.gov.cn/xinwen/site1/20150112/85091421054385123.pdf.
Data Product:
| ID |
Data Name |
Data Size |
Operation |
| 1 |
AtmWaterCycle&CloudWaterResChina.rar |
833.92KB |
|