Uncertainty in gridded precipitation products: Influence of station density, interpolation method and grid resolution

This work analyses three uncertainty sources affecting the observation-based gridded data sets: station density, interpolation methodology and spatial resolution. For this purpose, we consider precipitation in two countries, Poland and Spain, three resolutions (0.11, 0.22 and 0.44°), three interpola...

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Detalles Bibliográficos
Autores: Herrera, Sixto, Kotlarski, Sven, Soares, Pedro M. M., Cardoso, Rita M., Jaczewski, Adam, Gutiérrez, José M., Maraun, Douglas
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2019
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/213696
Acceso en línea:http://hdl.handle.net/10261/213696
Access Level:acceso abierto
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spelling Uncertainty in gridded precipitation products: Influence of station density, interpolation method and grid resolutionHerrera, SixtoKotlarski, SvenSoares, Pedro M. M.Cardoso, Rita M.Jaczewski, AdamGutiérrez, José M.Maraun, DouglasThis work analyses three uncertainty sources affecting the observation-based gridded data sets: station density, interpolation methodology and spatial resolution. For this purpose, we consider precipitation in two countries, Poland and Spain, three resolutions (0.11, 0.22 and 0.44°), three interpolation methods, both areal- and point-representative implementations, and three different densities of the underlying station network (high/medium/low density). As a result, for each resolution and interpolation approach, nine different grids have been obtained for each country and inter-compared using a variance decomposition methodology. Results indicate larger differences among the data sets for Spain than for Poland, mainly due to the larger spatial variability and complex orography of the former region. The variance decomposition points out to station density as the most influential factor, independent of the season, the areal- or point-representative implementation and the country considered, and slightly increasing with the spatial resolution. In contrast, the decomposition is stable when extreme precipitation indices are considered, in particular for the 50-year return value. Finally, the uncertainty due to station sub-sampling inside a particular grid box decreases with the number of stations used in the averaging/interpolation. In the case of spatially homogeneous grid boxes, the interpolation approach obtains similar results for all the parameters, excepting the wet day frequency, independently of the number of stations. When there is a more significant internal variability in the grid box, the interpolation is more sensitive to the number of stations, pointing out to a minimum stations’ density for the target resolution (six to seven stations).VALUE has been funded as EU COST Action ES1102. Participation of S.H. and J.M.G. was partially supported by the project MULTI‐SDM (CGL2015‐66583‐R, MINECO/FEDER). P.M.M.S. and R.M.C. wish to acknowledge the projects SOLAR (PTDC/GEOMET/7078/2014) and FCT UID/GEO/50019/ 2013 ‐ Instituto Dom Luiz, both financed by the Fundação para a Ciência e Tecnologia.John Wiley & SonsEuropean CommissionMinisterio de Economía y Competitividad (España)Fundação para a Ciência e a Tecnologia (Portugal)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2020202020192020info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/213696reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/CGL2015-66583-Rhttps://doi.org/10.1002/joc.5878Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2136962026-05-22T06:33:51Z
dc.title.none.fl_str_mv Uncertainty in gridded precipitation products: Influence of station density, interpolation method and grid resolution
title Uncertainty in gridded precipitation products: Influence of station density, interpolation method and grid resolution
spellingShingle Uncertainty in gridded precipitation products: Influence of station density, interpolation method and grid resolution
Herrera, Sixto
title_short Uncertainty in gridded precipitation products: Influence of station density, interpolation method and grid resolution
title_full Uncertainty in gridded precipitation products: Influence of station density, interpolation method and grid resolution
title_fullStr Uncertainty in gridded precipitation products: Influence of station density, interpolation method and grid resolution
title_full_unstemmed Uncertainty in gridded precipitation products: Influence of station density, interpolation method and grid resolution
title_sort Uncertainty in gridded precipitation products: Influence of station density, interpolation method and grid resolution
dc.creator.none.fl_str_mv Herrera, Sixto
Kotlarski, Sven
Soares, Pedro M. M.
Cardoso, Rita M.
Jaczewski, Adam
Gutiérrez, José M.
Maraun, Douglas
author Herrera, Sixto
author_facet Herrera, Sixto
Kotlarski, Sven
Soares, Pedro M. M.
Cardoso, Rita M.
Jaczewski, Adam
Gutiérrez, José M.
Maraun, Douglas
author_role author
author2 Kotlarski, Sven
Soares, Pedro M. M.
Cardoso, Rita M.
Jaczewski, Adam
Gutiérrez, José M.
Maraun, Douglas
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv European Commission
Ministerio de Economía y Competitividad (España)
Fundação para a Ciência e a Tecnologia (Portugal)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
description This work analyses three uncertainty sources affecting the observation-based gridded data sets: station density, interpolation methodology and spatial resolution. For this purpose, we consider precipitation in two countries, Poland and Spain, three resolutions (0.11, 0.22 and 0.44°), three interpolation methods, both areal- and point-representative implementations, and three different densities of the underlying station network (high/medium/low density). As a result, for each resolution and interpolation approach, nine different grids have been obtained for each country and inter-compared using a variance decomposition methodology. Results indicate larger differences among the data sets for Spain than for Poland, mainly due to the larger spatial variability and complex orography of the former region. The variance decomposition points out to station density as the most influential factor, independent of the season, the areal- or point-representative implementation and the country considered, and slightly increasing with the spatial resolution. In contrast, the decomposition is stable when extreme precipitation indices are considered, in particular for the 50-year return value. Finally, the uncertainty due to station sub-sampling inside a particular grid box decreases with the number of stations used in the averaging/interpolation. In the case of spatially homogeneous grid boxes, the interpolation approach obtains similar results for all the parameters, excepting the wet day frequency, independently of the number of stations. When there is a more significant internal variability in the grid box, the interpolation is more sensitive to the number of stations, pointing out to a minimum stations’ density for the target resolution (six to seven stations).
publishDate 2019
dc.date.none.fl_str_mv 2019
2020
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/213696
url http://hdl.handle.net/10261/213696
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/CGL2015-66583-R
https://doi.org/10.1002/joc.5878
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv John Wiley & Sons
publisher.none.fl_str_mv John Wiley & Sons
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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