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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 21, issue 1 | Copyright
Hydrol. Earth Syst. Sci., 21, 589-615, 2017
https://doi.org/10.5194/hess-21-589-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 30 Jan 2017

Research article | 30 Jan 2017

MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data

Hylke E. Beck1, Albert I. J. M. van Dijk2, Vincenzo Levizzani3, Jaap Schellekens4, Diego G. Miralles5,6, Brecht Martens5, and Ad de Roo1 Hylke E. Beck et al.
  • 1European Commission, Joint Research Centre (JRC), Via Enrico Fermi 2749, 21027 Ispra (VA), Italy
  • 2Fenner School of Environment & Society, The Australian National University, Canberra, Australia
  • 3National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Bologna, Italy
  • 4Inland Water Systems Unit, Deltares, Delft, the Netherlands
  • 5Laboratory of Hydrology and Water Management, Ghent University, Ghent, Belgium
  • 6Department of Earth Sciences, VU University Amsterdam, Amsterdam, the Netherlands

Abstract. Current global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a global P dataset for the period 1979–2015 with a 3-hourly temporal and 0.25° spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of timescale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite- and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0% of the stations and a median R of 0.67 vs. 0.44–0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments (<50000km2) across the globe. Specifically, we calibrated the simple conceptual hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) against daily Q observations with P from each of the different datasets. For the 1058 sparsely gauged catchments, representative of 83.9% of the global land surface (excluding Antarctica), MSWEP obtained a median calibration NSE of 0.52 vs. 0.29–0.39 for the other P datasets. MSWEP is available via http://www.gloh2o.org.

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MSWEP (Multi-Source Weighted-Ensem­ble Pre­cip­i­ta­tion) is a new global ter­res­trial pre­cip­i­ta­tion dataset with a high 3-hourly tem­po­ral and 0.25° spa­tial res­o­lu­tion. The dataset is unique in that it takes advan­tage of a wide range of data sources, includ­ing gauge, satel­lite, and reanaly­sis data, to obtain the best pos­si­ble precipitation esti­mates at global scale. The dataset outper­forms existing gauge-adjusted precipitation datasets.
MSWEP (Multi-Source Weighted-Ensem­ble Pre­cip­i­ta­tion) is a new global ter­res­trial...
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