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dc.contributor.authorHunziker, Stefan
dc.contributor.authorGubler, Stefanie
dc.contributor.authorCalle, J.
dc.contributor.authorMoreno, Isabel
dc.contributor.authorAndrade, Marcos
dc.contributor.authorVelarde, Fernando
dc.contributor.authorTicona, Laura
dc.contributor.authorCarrasco, Gualberto
dc.contributor.authorCastellón, Yaruska
dc.contributor.authorOria, Clara
dc.contributor.authorCroci-Maspoli, M.
dc.contributor.authorKonzelmann, Thomas
dc.contributor.authorRohrer, M.
dc.contributor.authorBrönnimann, Stefan
dc.date.accessioned2019-09-20T01:07:11Z
dc.date.available2019-09-20T01:07:11Z
dc.date.issued2017-03-20
dc.identifier.govdocPerú
dc.identifier.urihttps://hdl.handle.net/20.500.12542/151
dc.description.abstractIn situ climatological observations are essential for studies related to climate trends and extreme events. However, in many regions of the globe, observational records are affected by a large number of data quality issues. Assessing and controlling the quality of such datasets is an important, often overlooked aspect of climate research. Besides analysing the measurement data, metadata are important for a comprehensive data quality assessment. However, metadata are often missing, but may partly be reconstructed by suitable actions such as station inspections. This study identifies and attributes the most important common data quality issues in Bolivian and Peruvian temperature and precipitation datasets. The same or similar errors are found in many other predominantly manned station networks worldwide. A large fraction of these issues can be traced back to measurement errors by the observers. Therefore, the most effective way to prevent errors is to strengthen the training of observers and to establish a near real-time quality control (QC) procedure. Many common data quality issues are hardly detected by usual QC approaches. Data visualization, however, is an effective tool to identify and attribute those issues, and therefore enables data users to potentially correct errors and to decide which purposes are not affected by specific problems. The resulting increase in usable station records is particularly important in areas where station networks are sparse. In such networks, adequate selection and treatment of time series based on a comprehensive QC procedure may contribute to improving data homogeneity more than statistical data homogenization methods.en_US
dc.formatapplication/pdf
dc.language.isoengen_US
dc.publisherJohn Wiley and Sons Ltden_US
dc.relation.ispartofurn:issn:1097-0088
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Estados Unidos de América*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.sourceRepositorio Institucional - SENAMHIen_US
dc.sourceServicio Nacional de Meteorología e Hidrología del Perúen_US
dc.subjectClimatologíaen_US
dc.subjectData rescueen_US
dc.subjectError attributionen_US
dc.subjectQuality controlen_US
dc.subjectAnálisis de Datosen_US
dc.titleIdentifying, attributing, and overcoming common data quality issues of manned station observationsen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.isni0000 0001 0746 0446
dc.description.peerreviewPor pares
dc.identifier.doihttps://doi.org/10.1002/joc.5037.
dc.identifier.journalInternational Journal of Climatology
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.05.10


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