GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle
Open accessAbstract
In 1989, the need for reliable gridded land surface precipitation data sets, in view of the large uncertainties in the assessment of the global energy and water cycle, has led to the establishment of the Global Precipitation Climatology Centre (GPCC) at Deutscher Wetterdienst on invitation of the WMO. The GPCC has calculated a precipitation climatology for the global land areas for the target period 1951–2000 by objective analysis of climatological normals of about 67,200 rain gauge stations from its data base. GPCC's new precipitation climatology is compared to several other station-based precipitation climatologies as well as to precipitation climatologies derived from the GPCP V2.2 data set and from ECMWF's model reanalyses ERA-40 and ERA-Interim. Finally, how GPCC's best estimate for terrestrial mean precipitation derived from the precipitation climatology of 786 mm per year (equivalent to a water transport of 117,000 km 3 ) is fitting into the global water cycle context is discussed.
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Current quality control of surface precipitation data in China primarily relies on precipitation-threshold-based traditional methods, such as regional extreme value checks, temporal consistency checks, and spatial consistency comparisons.
“Currently, the quality control (QC) of surface precipitation data in China primarily relies on precipitation-threshold-based traditional methods, such as regional extreme value checks, temporal consistency checks, and spatial consistency comparisons between stations [16][17][18][19].”
From A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data (2025) · cites this paper as [18]
Cites
An Overview of the Global Historical Climatology Network-Daily Database
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., & Houston, T. G. (2012) · Journal of Atmospheric and Oceanic Technology · doi:10.1175/jtech-d-11-00103.1 · Cited by 1,993 · see citing works
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MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., van Dijk, A. I. J. M., McVicar, T. R., & Adler, R. F. (2019) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-17-0138.1 · Cited by 1,411 · see citing works
Müller Schmied, H., Eisner, S., Franz, D., Wattenbach, M., Portmann, F. T., Flörke, M., & Döll, P. (2014) · Hydrology and Earth System Sciences · doi:10.5194/hess-18-3511-2014 · Cited by 415 · see citing works
Drivers and impacts of Eastern African rainfall variability
Palmer, P. I., Wainwright, C. M., Dong, B., Maidment, R. I., Wheeler, K. G., Gedney, N., Hickman, J. E., Madani, N., Folwell, S. S., Abdo, G., et al. (2023) · Nature Reviews Earth & Environment · doi:10.1038/s43017-023-00397-x · Cited by 383 · see citing works
Jägermeyr, J., Pastor, A., Biemans, H., & Gerten, D. (2017) · Nature Communications · doi:10.1038/ncomms15900 · Cited by 334 · see citing works
Hwang, Y., Frierson, D. M. W., & Kang, S. M. (2013) · Geophysical Research Letters · doi:10.1002/grl.50502 · Cited by 266 · see citing works
Thomas, N., & Nigam, S. (2018) · Journal of Climate · doi:10.1175/jcli-d-17-0187.1 · Cited by 155 · see citing works
Assessing the observed impact of anthropogenic climate change
Hansen, G., & Stone, D. (2016) · Nature Climate Change · doi:10.1038/nclimate2896 · Cited by 139 · see citing works
ATTRICI v1.1 – counterfactual climate for impact attribution
Mengel, M., Treu, S., Lange, S., & Frieler, K. (2021) · Geoscientific Model Development · doi:10.5194/gmd-14-5269-2021 · Cited by 119 · see citing works
Ecosystem heterogeneity and diversity mitigate Amazon forest resilience to frequent extreme droughts
Longo, M., Knox, R. G., Levine, N. M., Alves, L. F., Bonal, D., Camargo, P. B., Fitzjarrald, D. R., Hayek, M. N., Restrepo‐Coupe, N., Saleska, S. R., et al. (2018) · New Phytologist · doi:10.1111/nph.15185 · Cited by 111 · see citing works
The Turkana low-level jet: mean climatology and association with regional aridity
Nicholson, S. (2016) · International Journal of Climatology · doi:10.1002/joc.4515 · Cited by 100 · see citing works
Quality control of a global hourly rainfall dataset
Lewis, E., Pritchard, D., Villalobos-Herrera, R., Blenkinsop, S., McClean, F., Guerreiro, S., Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., et al. (2021) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2021.105169 · Cited by 67 · see citing works
Self-citationInfluence of Mascarene high and Indian Ocean dipole on East African extreme weather events
Xing, L., & Ogou, K. (2015) · Geographica Pannonica · doi:10.5937/geopan1502064o · Cited by 57 · see citing works
Southward shift of the northern tropical belt from 1945 to 1980
Brönnimann, S., Fischer, A. M., Rozanov, E., Poli, P., Compo, G. P., & Sardeshmukh, P. D. (2015) · Nature Geoscience · doi:10.1038/ngeo2568 · Cited by 58 · see citing works
Dunstone, N., Smith, D. M., Hardiman, S. C., Davies, P., Ineson, S., Jain, S., Kent, C., Martin, G., & Scaife, A. A. (2023) · Nature Communications · doi:10.1038/s41467-023-42377-1 · Cited by 32 · see citing works
A Machine Learning-Based Quality Control Algorithm for Heavy Rainfall Using Multi-Source Data
Sun, H., Zhou, Q., Shi, L., Li, C., Qin, S., Yao, D., Xu, M., Huang, Y., Hu, Q., & Guan, Y. (2025) · Remote Sensing · doi:10.3390/rs17243976 · Cited by 4 · see citing works
- Authors
- Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Ziese, M., & Rudolf, B.
- Affiliations
- Deutscher Wetterdienst (DE)
- Corresponding author
- Schneider, Udo
- ORCID
- Becker, Andreas
- Venue
- Theoretical and Applied Climatology
- Published
- 2013-03-20
- Volume
- 115
- Issue
- 1-2
- Pages
- 15-40
- ISSN
- 0066-6424 (ISSN-L)
- 0177-798X
- 1434-4483
- OpenAlex
- W1989839776
- Cited by
- 1,385
- References
- 1
- Citations by year
- 2026: 49 · 2025: 81 · 2024: 73 · 2023: 95 · 2022: 136 · 2021: 113 · 2020: 148 · 2019: 144 · 2018: 124 · 2017: 124 · 2016: 96 · 2015: 103 · 2014: 80 · 2013: 17
- FWCI
- 64.30
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Theoretical and Applied Climatology (publishedVersion, cc-by)
- Funders
- Centrum fÖr Personcentrerad Vård
- Topics
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Precipitation Measurement and Analysis
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
- Keywords
- GPCC, gridded precipitation, rain gauge stations, quality control, global water cycle, water transport, GPCP, ERA-Interim reanalysis, ERA-40, climate normals, objective analysis
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