Detection of inhomogeneities in precipitation time series in Portugal using direct sequential simulation

Detalhes bibliográficos
Autor(a) principal: Ribeiro, Sara
Data de Publicação: 2016
Outros Autores: Caineta, Júlio, Costa, Ana Cristina, Henriques, Roberto, Soares, Amílcar
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://doi.org/10.1016/j.atmosres.2015.11.014
Resumo: Ribeiro, S., Caineta, J., Costa, A. C., Henriques, R., & Soares, A. (2016). Detection of inhomogeneities in precipitation time series in Portugal using direct sequential simulation. Atmospheric Research, 171, 147-158. https://doi.org/10.1016/j.atmosres.2015.11.014---------------- The authors gratefully acknowledge the financial support of “Fundação para a Ciência e Tecnologia” (FCT), Portugal, through the research project PTDC/GEO-MET/4026/2012 (“GSIMCLI – Geostatistical simulation with local distributions for the homogenisation and interpolation of climate data”).
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spelling Detection of inhomogeneities in precipitation time series in Portugal using direct sequential simulationDiscontinuitiesGeostatistical simulationHomogeneity testsHomogenizationAtmospheric ScienceSDG 13 - Climate ActionRibeiro, S., Caineta, J., Costa, A. C., Henriques, R., & Soares, A. (2016). Detection of inhomogeneities in precipitation time series in Portugal using direct sequential simulation. Atmospheric Research, 171, 147-158. https://doi.org/10.1016/j.atmosres.2015.11.014---------------- The authors gratefully acknowledge the financial support of “Fundação para a Ciência e Tecnologia” (FCT), Portugal, through the research project PTDC/GEO-MET/4026/2012 (“GSIMCLI – Geostatistical simulation with local distributions for the homogenisation and interpolation of climate data”).Climate data homogenisation is of major importance in climate change monitoring, validation of weather forecasting, general circulation and regional atmospheric models, modelling of erosion, drought monitoring, among other studies of hydrological and environmental impacts. The reason is that non-climate factors can cause time series discontinuities which may hide the true climatic signal and patterns, thus potentially bias the conclusions of those studies. In the last two decades, many methods have been developed to identify and remove these inhomogeneities. One of those is based on a geostatistical simulation technique (DSS - direct sequential simulation), where local probability density functions (pdfs) are calculated at candidate monitoring stations using spatial and temporal neighbouring observations, which then are used for the detection of inhomogeneities. Such approach has been previously applied to detect inhomogeneities in four precipitation series (wet day count) from a network with 66 monitoring stations located in the southern region of Portugal (1980-2001). That study revealed promising results and the potential advantages of geostatistical techniques for inhomogeneity detection in climate time series. This work extends the case study presented before and investigates the application of the geostatistical stochastic approach to ten precipitation series that were previously classified as inhomogeneous by one of six absolute homogeneity tests (Mann-Kendall, Wald-Wolfowitz runs, Von Neumann ratio, Pettitt, Buishand range test, and standard normal homogeneity test (SNHT) for a single break). Moreover, a sensitivity analysis is performed to investigate the number of simulated realisations which should be used to infer the local pdfs with more accuracy. Accordingly, the number of simulations per iteration was increased from 50 to 500, which resulted in a more representative local pdf. As in the previous study, the results are compared with those from the SNHT, Pettitt and Buishand range tests, which were applied to composite (ratio) reference series. The geostatistical procedure also allowed us to fill in missing values in the climate data series. Finally, based on several experiments aimed at providing a sensitivity analysis of the procedure, a set of default and recommended settings is provided, which will help other users to apply this method.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNRibeiro, SaraCaineta, JúlioCosta, Ana CristinaHenriques, RobertoSoares, Amílcar2019-07-25T22:17:43Z2016-05-012016-05-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article12application/pdfhttps://doi.org/10.1016/j.atmosres.2015.11.014eng0169-8095PURE: 2559591http://www.scopus.com/inward/record.url?scp=84954374333&partnerID=8YFLogxKhttps://doi.org/10.1016/j.atmosres.2015.11.014info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:34:51Zoai:run.unl.pt:10362/76514Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:35:38.382467Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Detection of inhomogeneities in precipitation time series in Portugal using direct sequential simulation
title Detection of inhomogeneities in precipitation time series in Portugal using direct sequential simulation
spellingShingle Detection of inhomogeneities in precipitation time series in Portugal using direct sequential simulation
Ribeiro, Sara
Discontinuities
Geostatistical simulation
Homogeneity tests
Homogenization
Atmospheric Science
SDG 13 - Climate Action
title_short Detection of inhomogeneities in precipitation time series in Portugal using direct sequential simulation
title_full Detection of inhomogeneities in precipitation time series in Portugal using direct sequential simulation
title_fullStr Detection of inhomogeneities in precipitation time series in Portugal using direct sequential simulation
title_full_unstemmed Detection of inhomogeneities in precipitation time series in Portugal using direct sequential simulation
title_sort Detection of inhomogeneities in precipitation time series in Portugal using direct sequential simulation
author Ribeiro, Sara
author_facet Ribeiro, Sara
Caineta, Júlio
Costa, Ana Cristina
Henriques, Roberto
Soares, Amílcar
author_role author
author2 Caineta, Júlio
Costa, Ana Cristina
Henriques, Roberto
Soares, Amílcar
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Ribeiro, Sara
Caineta, Júlio
Costa, Ana Cristina
Henriques, Roberto
Soares, Amílcar
dc.subject.por.fl_str_mv Discontinuities
Geostatistical simulation
Homogeneity tests
Homogenization
Atmospheric Science
SDG 13 - Climate Action
topic Discontinuities
Geostatistical simulation
Homogeneity tests
Homogenization
Atmospheric Science
SDG 13 - Climate Action
description Ribeiro, S., Caineta, J., Costa, A. C., Henriques, R., & Soares, A. (2016). Detection of inhomogeneities in precipitation time series in Portugal using direct sequential simulation. Atmospheric Research, 171, 147-158. https://doi.org/10.1016/j.atmosres.2015.11.014---------------- The authors gratefully acknowledge the financial support of “Fundação para a Ciência e Tecnologia” (FCT), Portugal, through the research project PTDC/GEO-MET/4026/2012 (“GSIMCLI – Geostatistical simulation with local distributions for the homogenisation and interpolation of climate data”).
publishDate 2016
dc.date.none.fl_str_mv 2016-05-01
2016-05-01T00:00:00Z
2019-07-25T22:17:43Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://doi.org/10.1016/j.atmosres.2015.11.014
url https://doi.org/10.1016/j.atmosres.2015.11.014
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0169-8095
PURE: 2559591
http://www.scopus.com/inward/record.url?scp=84954374333&partnerID=8YFLogxK
https://doi.org/10.1016/j.atmosres.2015.11.014
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 12
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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