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

Abstract

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 13 762 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 (< 50 000 km 2 ) 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.

Near-real-time adjusted reanalysis forcing data for hydrology

“…Because WFDEI was on several occasions evaluated against flux-tower measurements across the globe (Weedon et al, , 2014Beck et al, 2017), we do not repeat such an evaluation for GFDCL here and compare instead to the WFDEI and other data sets.…”

“…Reanalysis data fulfill the spatial and temporal consistency but suffer from bias that limits their use for hydrological simulations. Current data sets that merge reanalysis and coarser observations bridge the data gap but are mostly only episodically updated (Sheffield et al, 2006;Weedon et al, , 2014Beck et al, 2017).…”

“…Earlier methods (Sheffield et al, 2006;Weedon et al, , 2014Beck et al, 2017) have merged information from a reanalysis with temporally coarser observational data to produce new data sets that inherit the temporal resolution of the reanalysis with the average properties of the observations. With these methods, long periods of internally consistent daily or sub-daily resolution and global coverage become available for, e.g., large-scale hydrological simulations.…”

Contributions of different bias‐correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes

“…First, our analysis does not include the latest forcing data sets, AgMERRA and AgCFSR [Ruane et al, 2015], used in the Agricultural Model Intercomparison and Improvement Project. A sophisticated retrospective forcing data set for precipitation (MSWEP), which is a hybrid of gauge observations, satellite estimates, and reanalysis data, has become recently available [Beck et al, 2017], although this study only considered forcing data sets that cover the many climatic variables necessary to run impact models. An analysis of precipitation indices calculated using MSWEB forcing data set might be valuable.…”

<span>The Mediterranean moisture supply in the genesis of climatological and extreme monthly continental precipitation</span>

“…In this work we used the monthly precipitation dataset Multi-Source Weighted-Ensemble Precipitation (MSWEP) [33][34] from January 1980 to December 2015, although only results for September were shown and described. MSWEP is a new precipitation dataset, which has been accessible since the beginning of 2017.…”