Rainfall Estimates on a Gridded Network (REGEN) based on all station data Version 1-2019

A global land-based gridded dataset of daily precipitation at 1 degree X 1 degree resolution from 1950 to 2016. Two datasets are available under the REGEN moniker. This dataset interpolates all daily precipitation stations available regardless of completeness of station timeseries. A second related...

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Main Author: Contractor, Steefan
Format: Other/Unknown Material
Language:English
Published: Zenodo 2019
Subjects:
Online Access:https://doi.org/10.25914/5ca4c380b0d44
https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f8555_9260_4736_9502
https://researchdata.ands.org.au/rainfall-estimates-gridded-v1-2019/1408744
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spelling ftzenodo:oai:zenodo.org:4922160 2024-09-15T17:42:13+00:00 Rainfall Estimates on a Gridded Network (REGEN) based on all station data Version 1-2019 Contractor, Steefan 2019-06-28 https://doi.org/10.25914/5ca4c380b0d44 https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f8555_9260_4736_9502 https://researchdata.ands.org.au/rainfall-estimates-gridded-v1-2019/1408744 eng eng Zenodo https://doi.org/10.5194/hess-2018-595 https://zenodo.org/communities/arc-coe-clex-data https://doi.org/10.25914/5ca4c380b0d44 oai:zenodo.org:4922160 https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f8555_9260_4736_9502 https://researchdata.ands.org.au/rainfall-estimates-gridded-v1-2019/1408744 info:eu-repo/semantics/openAccess Creative Commons Attribution Non Commercial Share Alike 4.0 International https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode Precipitation Observations info:eu-repo/semantics/other 2019 ftzenodo https://doi.org/10.25914/5ca4c380b0d4410.5194/hess-2018-595 2024-07-26T21:16:34Z A global land-based gridded dataset of daily precipitation at 1 degree X 1 degree resolution from 1950 to 2016. Two datasets are available under the REGEN moniker. This dataset interpolates all daily precipitation stations available regardless of completeness of station timeseries. A second related dataset available here (update link)interpolates only the long-term stations (stations with at least 40 complete years of data). Besides the grid cell average precipitation amount per day (mm/day), the Yamamoto standard deviation per grid cell (mm/day), the kriging error per grid cell (%) and number of stations per grid cell variables are also included. Currently, there are two major data archives of global in situ daily rainfall data: 1. The Global Historical Station Network (GHCN-Daily) hosted by National Oceanic and Atmospheric Administration (NOAA) and 2. The Deutscher Wetterdienst (DWD) Global Precipitation Climatology Centre (GPCC). These two data archives are combined along with additional station data acquired from other researchers. The merged archive is quality controlled and the flagged stations are removed to create a long term high quality archive of raw station data, which is then interpolated using ordinary block kriging by this dataset. The REGEN long-term dataset instead only interpolates a long-term station subset of this high quality merged archive. The output consists of a separate CF-compliant netcdf file for each year, each containing the four aforementioned variables. These values are available for global land areas with the exception of Antarctica. The time dimension of the netcdf ranges from 1950-01-01 to 2016-12-31. Each dataset (All station based and long-term station based) is in classic netcdf format and occupies around 350MB of disk space for each year with the combined total of all years being around 26GB. Besides these two netcdfs, two additional netcdfs containing a mask indicating the high data quality grid cells of each dataset (all station and long-term) are also available. These ... Other/Unknown Material Antarc* Antarctica Zenodo
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
language English
topic Precipitation
Observations
spellingShingle Precipitation
Observations
Contractor, Steefan
Rainfall Estimates on a Gridded Network (REGEN) based on all station data Version 1-2019
topic_facet Precipitation
Observations
description A global land-based gridded dataset of daily precipitation at 1 degree X 1 degree resolution from 1950 to 2016. Two datasets are available under the REGEN moniker. This dataset interpolates all daily precipitation stations available regardless of completeness of station timeseries. A second related dataset available here (update link)interpolates only the long-term stations (stations with at least 40 complete years of data). Besides the grid cell average precipitation amount per day (mm/day), the Yamamoto standard deviation per grid cell (mm/day), the kriging error per grid cell (%) and number of stations per grid cell variables are also included. Currently, there are two major data archives of global in situ daily rainfall data: 1. The Global Historical Station Network (GHCN-Daily) hosted by National Oceanic and Atmospheric Administration (NOAA) and 2. The Deutscher Wetterdienst (DWD) Global Precipitation Climatology Centre (GPCC). These two data archives are combined along with additional station data acquired from other researchers. The merged archive is quality controlled and the flagged stations are removed to create a long term high quality archive of raw station data, which is then interpolated using ordinary block kriging by this dataset. The REGEN long-term dataset instead only interpolates a long-term station subset of this high quality merged archive. The output consists of a separate CF-compliant netcdf file for each year, each containing the four aforementioned variables. These values are available for global land areas with the exception of Antarctica. The time dimension of the netcdf ranges from 1950-01-01 to 2016-12-31. Each dataset (All station based and long-term station based) is in classic netcdf format and occupies around 350MB of disk space for each year with the combined total of all years being around 26GB. Besides these two netcdfs, two additional netcdfs containing a mask indicating the high data quality grid cells of each dataset (all station and long-term) are also available. These ...
format Other/Unknown Material
author Contractor, Steefan
author_facet Contractor, Steefan
author_sort Contractor, Steefan
title Rainfall Estimates on a Gridded Network (REGEN) based on all station data Version 1-2019
title_short Rainfall Estimates on a Gridded Network (REGEN) based on all station data Version 1-2019
title_full Rainfall Estimates on a Gridded Network (REGEN) based on all station data Version 1-2019
title_fullStr Rainfall Estimates on a Gridded Network (REGEN) based on all station data Version 1-2019
title_full_unstemmed Rainfall Estimates on a Gridded Network (REGEN) based on all station data Version 1-2019
title_sort rainfall estimates on a gridded network (regen) based on all station data version 1-2019
publisher Zenodo
publishDate 2019
url https://doi.org/10.25914/5ca4c380b0d44
https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f8555_9260_4736_9502
https://researchdata.ands.org.au/rainfall-estimates-gridded-v1-2019/1408744
genre Antarc*
Antarctica
genre_facet Antarc*
Antarctica
op_relation https://doi.org/10.5194/hess-2018-595
https://zenodo.org/communities/arc-coe-clex-data
https://doi.org/10.25914/5ca4c380b0d44
oai:zenodo.org:4922160
https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f8555_9260_4736_9502
https://researchdata.ands.org.au/rainfall-estimates-gridded-v1-2019/1408744
op_rights info:eu-repo/semantics/openAccess
Creative Commons Attribution Non Commercial Share Alike 4.0 International
https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
op_doi https://doi.org/10.25914/5ca4c380b0d4410.5194/hess-2018-595
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