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      GPCC Full Data Reanalysis Version 7.0 at 2.5°: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data

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          Abstract

          This is the centennial GPCC Full Data Reanalysis of monthly global land-surface precipitation based on the 75,000 stations world-wide that feature record durations of 10 years or longer. This product contains the monthly totals on a regular grid with a spatial resolution of 2.5° x 2.5° latitude by longitude. Precipitation anomalies at the stations are interpolated and then superimposed on the GPCC Climatology V2015 in the corresponding resolution (DOI: 10.5676/DWD_GPCC/CLIM_M_V2015_250). The temporal coverage of the dataset ranges from January 1901 until December 2013. The GPCC Full Data Reanalysis is the most accurate in situ precipitation reanalysis data set of GPCC. Moreover it supports regional climate monitoring, model validation, climate variability analysis and water resources assessment studies.

          Abstract

          General user recommendations: Users are referred to following gridded Global Precipitation Climate Centre (GPCC) products: GPCC Climatology 1951-2000 (GPCC_CLIM) with high representativeness, for application as a reference, and for utilization of the anomaly interpolation method. GPCC Full Data Reanalysis (GPCC_FD) with high accuracy, for, e.g., verification of models, or for analysis of historic global precipitation, or for research concerning the global water cycle, e.g., trend and teleconnection analyses. GPCC Monitoring Product (GPCC_MP) with high availability and reasonable timeliness, for applications like calibration of satellite data, or early annual reporting. GPCC First Guess Product (GPCC_FG) with high timeliness, to be applied for, e.g., drought monitoring.

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          Author and article information

          Journal
          Global Precipitation Climatology Centre (GPCC) at Deutscher Wetterdienst
          2015
          01 January 1901
          Article
          10.5676/DWD_GPCC/FD_M_V7_250
          9dcf7d0e-4ce0-4d0f-ad03-93e73412570e
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