A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons
Abstract
Abstract In this paper, we present a comprehensive review of the data sources and estimation methods of 30 currently available global precipitation data sets, including gauge‐based, satellite‐related, and reanalysis data sets. We analyzed the discrepancies between the data sets from daily to annual timescales and found large differences in both the magnitude and the variability of precipitation estimates. The magnitude of annual precipitation estimates over global land deviated by as much as 300 mm/yr among the products. Reanalysis data sets had a larger degree of variability than the other types of data sets. The degree of variability in precipitation estimates also varied by region. Large differences in annual and seasonal estimates were found in tropical oceans, complex mountain areas, northern Africa, and some high‐latitude regions. Overall, the variability associated with extreme precipitation estimates was slightly greater at lower latitudes than at higher latitudes. The reliability of precipitation data sets is mainly limited by the number and spatial coverage of surface stations, the satellite algorithms, and the data assimilation models. The inconsistencies described limit the capability of the products for climate monitoring, attribution, and model validation.
- Authors:
-
[1];
[1];
[2];
[2];
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science Beijing Normal University Beijing China
- Department of Civil and Environmental Engineering University of California Irvine CA USA
- Publication Date:
- Research Org.:
- Univ. of California, Oakland, CA (United States)
- Sponsoring Org.:
- USDOE Office of International Affairs (IA)
- OSTI Identifier:
- 1416236
- Alternate Identifier(s):
- OSTI ID: 1416237; OSTI ID: 1537332
- Grant/Contract Number:
- DE‐IA0000018; IA0000018
- Resource Type:
- Published Article
- Journal Name:
- Reviews of Geophysics (1985)
- Additional Journal Information:
- Journal Name: Reviews of Geophysics (1985) Journal Volume: 56 Journal Issue: 1; Journal ID: ISSN 8755-1209
- Publisher:
- American Geophysical Union (AGU)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; Geochemistry & Geophysics
Citation Formats
Sun, Qiaohong, Miao, Chiyuan, Duan, Qingyun, Ashouri, Hamed, Sorooshian, Soroosh, and Hsu, Kuo‐Lin. A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons. United States: N. p., 2018.
Web. doi:10.1002/2017RG000574.
Sun, Qiaohong, Miao, Chiyuan, Duan, Qingyun, Ashouri, Hamed, Sorooshian, Soroosh, & Hsu, Kuo‐Lin. A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons. United States. https://doi.org/10.1002/2017RG000574
Sun, Qiaohong, Miao, Chiyuan, Duan, Qingyun, Ashouri, Hamed, Sorooshian, Soroosh, and Hsu, Kuo‐Lin. Fri .
"A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons". United States. https://doi.org/10.1002/2017RG000574.
@article{osti_1416236,
title = {A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons},
author = {Sun, Qiaohong and Miao, Chiyuan and Duan, Qingyun and Ashouri, Hamed and Sorooshian, Soroosh and Hsu, Kuo‐Lin},
abstractNote = {Abstract In this paper, we present a comprehensive review of the data sources and estimation methods of 30 currently available global precipitation data sets, including gauge‐based, satellite‐related, and reanalysis data sets. We analyzed the discrepancies between the data sets from daily to annual timescales and found large differences in both the magnitude and the variability of precipitation estimates. The magnitude of annual precipitation estimates over global land deviated by as much as 300 mm/yr among the products. Reanalysis data sets had a larger degree of variability than the other types of data sets. The degree of variability in precipitation estimates also varied by region. Large differences in annual and seasonal estimates were found in tropical oceans, complex mountain areas, northern Africa, and some high‐latitude regions. Overall, the variability associated with extreme precipitation estimates was slightly greater at lower latitudes than at higher latitudes. The reliability of precipitation data sets is mainly limited by the number and spatial coverage of surface stations, the satellite algorithms, and the data assimilation models. The inconsistencies described limit the capability of the products for climate monitoring, attribution, and model validation.},
doi = {10.1002/2017RG000574},
journal = {Reviews of Geophysics (1985)},
number = 1,
volume = 56,
place = {United States},
year = {Fri Jan 05 00:00:00 EST 2018},
month = {Fri Jan 05 00:00:00 EST 2018}
}
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