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.
“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
The introduction (p. 1) says MSWEP combines gauge-, satellite-, and reanalysis-based data. The assessment (pp. 12, 17) reports that MSWEP V2 performed best overall in a 3-hourly CONUS comparison, but that comparison did not include every product used to build MSWEP. It therefore does not establish that MSWEP performs better than all its input products. (LLM, conf=0.89)
Experimental — accuracy not yet measuredTo ensure station observations undergo strict and comprehensive quality control, the study adopted the methods used to produce previous station-based datasets.
“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)
The MSWEP V2 methodology (p. 3) says extensive quality control was applied to gauge observations, and the appendix (p. 18) details the checks. It does not say those checks were adopted from methods used to produce previous station-based datasets; for erroneous-zero records, it says the authors developed an automated procedure. (LLM, conf=0.84)
Experimental — accuracy not yet measuredFor precipitation, quality control procedures consist of three parts, adopting methods from Durre et al. (2010), Hamada et al. (2011), and Beck et al. (2019).
“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)
The Appendix (p. 18) describes precipitation-gauge quality-control methods, including checks for erroneous zero rainfall and removal of suspect observations and gauges. It does not describe quality control as consisting of three parts; its erroneous-zero procedure alone has five steps. Beck et al. therefore supports the use of precipitation quality-control methods, but not the stated three-part structure. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe notable differences between MSWEP and SCDNA at national borders are because MSWEP infers gauge reporting times by searching for the highest correlation between gauge data and the temporally shifted reanalysis/satellite estimates.
“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
The appendix section “Inferring gauge reporting times” (p. 18) confirms that MSWEP selects the temporal offset with the highest correlation between gauge observations and shifted gridded precipitation data. The cited passage does not establish that this procedure causes differences between MSWEP and SCDNA at national borders. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe method of Beck et al. (2019) 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.
“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)
The appendix (p. 18) describes Beck et al.’s method for inferring precipitation-gauge reporting times, so the citation identifies the method. It does not assess the method for temperature, report that estimated lag days are mostly zero, or say that inferred times cannot be applied directly to adjust temperature-station observations. (LLM, conf=0.84)
Experimental — accuracy not yet measured
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.
“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)
For precipitation, quality control strategies in SCDNA are from three studies.
“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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Deep Learning for Improving Numerical Weather Prediction of Heavy Rainfall
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Self-citationThe Influence of Topography on the Global Terrestrial Water Cycle
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Assessing the Steady‐State Assumption in Water Balance Calculation Across Global Catchments
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- 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)
- Fenner School of Environment and Society, Australian National University, Canberra, Australian Capital Territory, Australia
- 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 · 2018: 2
- 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
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