HOAPS/GPCC global daily precipitation data record with uncertainty estimates using satellite and gauge based observations at 1.0° : Version 1
This dataset contains global precipitation observations combined from satellite and rain gauge data over ocean and over land, respectively. It spans the years 1988-2008 and has a spatial resolution of 1.0°×1.0°. Satellite observations are based on SSMI/I data of the HOAPS-3.2 dataset of CM SAF. Thes...
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Global Precipitation Climatology Centre (GPCC) at Deutscher Wetterdienst
2015
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Online Access: | https://dx.doi.org/10.5676/dwd_cdc/hogp_100/v001 https://opendata.dwd.de/climate_environment/GPCC/html/HOGP_V001.html |
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ftdatacite:10.5676/dwd_cdc/hogp_100/v001 2023-05-15T18:18:34+02:00 HOAPS/GPCC global daily precipitation data record with uncertainty estimates using satellite and gauge based observations at 1.0° : Version 1 Andersson, Axel Ziese, Markus Dietzsch, Felix Schröder, Marc Becker, Andreas Schamm, Kirstin 2015 compressed NetCDF https://dx.doi.org/10.5676/dwd_cdc/hogp_100/v001 https://opendata.dwd.de/climate_environment/GPCC/html/HOGP_V001.html unknown Global Precipitation Climatology Centre (GPCC) at Deutscher Wetterdienst dataset Dataset grid 2015 ftdatacite https://doi.org/10.5676/dwd_cdc/hogp_100/v001 2021-11-05T12:55:41Z This dataset contains global precipitation observations combined from satellite and rain gauge data over ocean and over land, respectively. It spans the years 1988-2008 and has a spatial resolution of 1.0°×1.0°. Satellite observations are based on SSMI/I data of the HOAPS-3.2 dataset of CM SAF. These data have been extended with observations from the TMI microwave imager. The basic rain rate retrieval is based on a neuronal network approach. The retrieval excludes sea-ice covered regions. Uncertainty estimations are given as standard deviation of pixel values in one grid cell. Precipitation observations over land are included from the GPPC Full Data Daily product (DOI: 10.5676/DWD_GPCC/FD_D_V1_100). Relative precipitation anomalies at the stations are interpolated by means of ordinary block kriging. For these data, uncertainty information is included as well. On coastal edges, data gaps are interpolated. This dataset is recommended to be used for analyses of extreme events and related statistics at daily resolution as well as for verification and validation purposes of other precipitation products or climate models. : This dataset has been created within the DAPACLIP project (Daily Precipitation Analysis for Climate Prediction). The DAPACLIP project was part of the MiKlip project framework (Mittelfristige Klimaprognosen) and funded by the German Federal Ministry of Education and Research (Bundesministerium fuer Bildung und Forschung, BMBF). Dataset Sea ice DataCite Metadata Store (German National Library of Science and Technology) |
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description |
This dataset contains global precipitation observations combined from satellite and rain gauge data over ocean and over land, respectively. It spans the years 1988-2008 and has a spatial resolution of 1.0°×1.0°. Satellite observations are based on SSMI/I data of the HOAPS-3.2 dataset of CM SAF. These data have been extended with observations from the TMI microwave imager. The basic rain rate retrieval is based on a neuronal network approach. The retrieval excludes sea-ice covered regions. Uncertainty estimations are given as standard deviation of pixel values in one grid cell. Precipitation observations over land are included from the GPPC Full Data Daily product (DOI: 10.5676/DWD_GPCC/FD_D_V1_100). Relative precipitation anomalies at the stations are interpolated by means of ordinary block kriging. For these data, uncertainty information is included as well. On coastal edges, data gaps are interpolated. This dataset is recommended to be used for analyses of extreme events and related statistics at daily resolution as well as for verification and validation purposes of other precipitation products or climate models. : This dataset has been created within the DAPACLIP project (Daily Precipitation Analysis for Climate Prediction). The DAPACLIP project was part of the MiKlip project framework (Mittelfristige Klimaprognosen) and funded by the German Federal Ministry of Education and Research (Bundesministerium fuer Bildung und Forschung, BMBF). |
format |
Dataset |
author |
Andersson, Axel Ziese, Markus Dietzsch, Felix Schröder, Marc Becker, Andreas Schamm, Kirstin |
spellingShingle |
Andersson, Axel Ziese, Markus Dietzsch, Felix Schröder, Marc Becker, Andreas Schamm, Kirstin HOAPS/GPCC global daily precipitation data record with uncertainty estimates using satellite and gauge based observations at 1.0° : Version 1 |
author_facet |
Andersson, Axel Ziese, Markus Dietzsch, Felix Schröder, Marc Becker, Andreas Schamm, Kirstin |
author_sort |
Andersson, Axel |
title |
HOAPS/GPCC global daily precipitation data record with uncertainty estimates using satellite and gauge based observations at 1.0° : Version 1 |
title_short |
HOAPS/GPCC global daily precipitation data record with uncertainty estimates using satellite and gauge based observations at 1.0° : Version 1 |
title_full |
HOAPS/GPCC global daily precipitation data record with uncertainty estimates using satellite and gauge based observations at 1.0° : Version 1 |
title_fullStr |
HOAPS/GPCC global daily precipitation data record with uncertainty estimates using satellite and gauge based observations at 1.0° : Version 1 |
title_full_unstemmed |
HOAPS/GPCC global daily precipitation data record with uncertainty estimates using satellite and gauge based observations at 1.0° : Version 1 |
title_sort |
hoaps/gpcc global daily precipitation data record with uncertainty estimates using satellite and gauge based observations at 1.0° : version 1 |
publisher |
Global Precipitation Climatology Centre (GPCC) at Deutscher Wetterdienst |
publishDate |
2015 |
url |
https://dx.doi.org/10.5676/dwd_cdc/hogp_100/v001 https://opendata.dwd.de/climate_environment/GPCC/html/HOGP_V001.html |
genre |
Sea ice |
genre_facet |
Sea ice |
op_doi |
https://doi.org/10.5676/dwd_cdc/hogp_100/v001 |
_version_ |
1766195190759948288 |