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MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment
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Abstract We present Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2), a gridded precipitation P dataset spanning 1979–2017. MSWEP V2 is unique in several aspects: i) full global coverage (all land and oceans); ii) high spatial (0.1°) and temporal (3 hourly) resolution; iii) optimal merging of P estimates based on gauges [WorldClim, Global Historical Climatology Network-Daily (GHCN-D), Global Summary of the Day (GSOD), Global Precipitation Climatology Centre (GPCC), and others], satellites [Climate Prediction Center morphing technique (CMORPH), Gridded Satellite (GridSat), Global Satellite Mapping of Precipitation (GSMaP), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT)], and reanalyses [European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) and Japanese 55-year Reanalysis (JRA-55)]; iv) distributional bias corrections, mainly to improve the P frequency; v) correction of systematic terrestrial P biases using river discharge Q observations from 13,762 stations across the globe; vi) incorporation of daily observations from 76,747 gauges worldwide; and vii) correction for regional differences in gauge reporting times. MSWEP V2 compares substantially better with Stage IV gauge–radar P data than other state-of-the-art P datasets for the United States, demonstrating the effectiveness of the MSWEP V2 methodology. Global comparisons suggest that MSWEP V2 exhibits more realistic spatial patterns in mean, magnitude, and frequency. Long-term mean P estimates for the global, land, and ocean domains based on MSWEP V2 are 955, 781, and 1,025 mm yr −1 , respectively. Other P datasets consistently underestimate P amounts in mountainous regions. Using MSWEP V2, P was estimated to occur 15.5%, 12.3%, and 16.9% of the time on average for the global, land, and ocean domains, respectively. MSWEP V2 provides unique opportunities to explore spatiotemporal variations in P , improve our understanding of hydrological processes and their parameterization, and enhance hydrological model performance.
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“MSWEP merges data from ground observations, satellite products, and reanalysis models and performs better than all products used for merging (Beck et al., 2019).”
From SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018 (2020) · cites this paper as Beck et al., 2019
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“To ensure station observations undergo strict and comprehensive quality control, we adopted the methods used to produce previous station-based datasets.”
From SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018 (2020) · cites this paper as Beck et al. (2019)
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“For precipitation, quality control procedures consist of three parts.”
From SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018 (2020) · cites this paper as Beck et al. (2019)
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“This is because MSWEP infers gauge reporting times by searching for the highest correlation between gauge data and the temporally shifted reanalysis/satellite estimates (Beck et al., 2019).”
From SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018 (2020) · cites this paper as Beck et al., 2019
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“However, this method may be unsuitable for temperature because the estimated lag day is mostly zero, and the inferred reporting time cannot be directly applied to adjust station observations.”
From SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018 (2020) · cites this paper as Beck et al. (2019)
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“Beck et al. (2019) inferred the reporting time of daily precipitation observations by calculating the SCC between the series of stations and gridded products, which is useful to correct the bias of gridded products.”
From SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018 (2020) · cites this paper as Beck et al. (2019)
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“For precipitation, quality control strategies are from three studies.”
From SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018 (2020) · cites this paper as Beck et al. (2019)
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Metadata
- Authors
- 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.
- Affiliations
- Australian National University (AU)
- Princeton University (US)
- Earth System Science Interdisciplinary Center (US)
- ORCID
- Venue
- Bulletin of the American Meteorological Society
- Publisher
- American Meteorological Society
- Published
- 2019-03
- Volume
- 100
- Issue
- 3
- Pages
- 473-500
- ISSN
- 0003-0007 (ISSN-L)
- 1520-0477
- OpenAlex
- W2890873664
- Cited by
- 1,411
- References
- 125
- Citations by year
- 2026: 246 · 2025: 259 · 2024: 236 · 2023: 215 · 2022: 176 · 2021: 150 · 2020: 88 · 2019: 39
- FWCI
- 33.99
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bulletin of the American Meteorological Society (publishedVersion)
- Flanders Marine Institute (Flanders Marine Institute) (publishedVersion, other-oa)
- Socio-Environmental Systems Modeling (acceptedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- EGUGA
- Topics
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Precipitation Measurement and Analysis
- Physical Sciences › Environmental Science › Environmental Engineering › Soil Moisture and Remote Sensing
- Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
- Keywords
- gridded precipitation, precipitation datasets, satellite precipitation, reanalysis precipitation, precipitation frequency, hydrological model performance, CMORPH, GSMaP, ERA-Interim reanalysis, JRA-55
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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