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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2018-595
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2018-595
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 30 Jan 2019

Research article | 30 Jan 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Rainfall Estimates on a Gridded Network (REGEN) – A global land-based gridded dataset of daily precipitation from 1950–2013

Steefan Contractor1,2, Markus G. Donat1,3,4, Lisa V. Alexander1,3, Markus Ziese5, Anja Meyer-Christoffer5, Udo Schneider5, Elke Rustemeier5, Andreas Becker5, Imke Durre6, and Russell S. Vose6 Steefan Contractor et al.
  • 1Climate Change Research Centre, UNSW Sydney, Australia
  • 2ARC Centre of Excellence for Climate System Science
  • 3ARC Centre of Excellence for Climate Extremes
  • 4Barcelona Supercomputing Center, Barcelona, Spain
  • 5Global Precipitation Climatology Centre, Deutscher Wetterdienst, Offenbach Germany
  • 6National Centers for Environmental Information, National Oceanic and Atmospheric Administration, Asheville NC, USA

Abstract. We present a new global land-based daily precipitation dataset from 1950 using an interpolated network of in situ data called Rainfall Estimates on a GriddEd Network – REGEN. We merged multiple archives of in situ data including two of the largest archives, the Global Historical Climatology Network – Daily (GHCN-Daily) hosted by National Centres of Environmental Information (NCEI), USA and one hosted by the Global Precipitation Climatology Centre (GPCC) operated by Deutscher Wetterdienst (DWD). This resulted in an unprecedented station density compared to existing datasets. The station timeseries were quality controlled using strict criteria and flagged values were removed. Remaining values were interpolated to create area average estimates of daily precipitation for global land areas on a 1° × 1° latitude–longitude resolution. Besides the daily precipitation amounts, fields of standard deviation, Kriging error and number of stations are also provided. We also provide a quality mask based on these uncertainty measures. For those interested in a dataset with lower station network variability we also provide a related dataset based on a network of long-term stations which interpolates stations with a record length of at least 40 years. The REGEN datasets are expected to contribute to the advancement of hydrological science and practice by facilitating studies aiming to understand changes and variability in several aspects of daily precipitation distributions, extremes, and measures of hydrological intensity. Here we document the development of the dataset and guidelines for best practices for users with regards to the two datasets.

Steefan Contractor et al.
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Steefan Contractor et al.
Data sets

Rainfall Estimates on a Gridded Network based on all station data v1.0 S. Contractor, M. G. Donat, L. V. Alexander, M. Ziese, A. Meyer-Christoffer, U. Schneider, E. Rustemeier, A. Becker, I. Durre, and R. S. Vose https://doi.org/10.25914/5b9fa55a8298c

Rainfall Estimates on a Gridded Network based on long-term station data v1.0 S. Contractor, M. G. Donat, L. V. Alexander, M. Ziese, A. Meyer-Christoffer, U. Schneider, E. Rustemeier, A. Becker, I. Durre, and R. S. Vose https://doi.org/10.25914/5b9fa67fce5d6

Steefan Contractor et al.
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Short summary
This paper provides the documentation of the REGEN dataset, a global land-based daily observational precipitation dataset from 1950 to 2013 at a gridded resolution of 1° × 1°. REGEN is the currently longest running global dataset of daily precipitation, and is expected to facilitate studies understanding changes and variability in several aspects of daily precipitation distributions, extremes, and measures of hydrological intensity.
This paper provides the documentation of the REGEN dataset, a global land-based daily...
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