MSWEP: 3-Hourly 0.25° Global Gridded Precipitation (1979-2015) by Merging Gauge, Satellite, and Reanalysis Data. Beck, H. E., van Dijk, A. I. J. M., Levizzani, V., Schellekens, J., Miralles, D. G., Martens, B., & de Roo, A.
MSWEP: 3-Hourly 0.25° Global Gridded Precipitation (1979-2015) by Merging Gauge, Satellite, and Reanalysis Data [link]Paper  doi  abstract   bibtex   
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), 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 time scale 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 versus 0.44-0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments ($<$ 50,000 km2) across the globe. Specifically, we calibrated the simple conceptual hydrological model HBV 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 versus 0.29-0.39 for the other P datasets. MSWEP is available via http://www.gloh2o.org.
@article{beckMSWEP3hourly252016,
  title = {{{MSWEP}}: 3-Hourly 0.25° Global Gridded Precipitation (1979-2015) by Merging Gauge, Satellite, and Reanalysis Data},
  author = {Beck, Hylke E. and van Dijk, Albert I. J. M. and Levizzani, Vincenzo and Schellekens, Jaap and Miralles, Diego G. and Martens, Brecht and de Roo, Ad},
  date = {2016-05},
  journaltitle = {Hydrology and Earth System Sciences Discussions},
  pages = {1--38},
  issn = {1812-2116},
  doi = {10.5194/hess-2016-236},
  url = {http://mfkp.org/INRMM/article/14103191},
  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), 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 time scale 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 versus 0.44-0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments ({$<$} 50,000 km2) across the globe. Specifically, we calibrated the simple conceptual hydrological model HBV 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 versus 0.29-0.39 for the other P datasets. MSWEP is available via http://www.gloh2o.org.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14103191,~to-add-doi-URL,bias-correction,environmental-modelling,featured-publication,global-scale,gridded-data,integration-techniques,open-data,precipitation,time-series},
  options = {useprefix=true}
}
Downloads: 0
About
Documentation
Keywords
Search
Users
Terms and Conditions of Use
Privacy Policy
Sitemap
© BibBase.org 2021