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Benchmarking homogenization algorithms for monthly data

  • Datos identificativos

    Identificador: imarina:6386846
    Handle: https://hdl.handle.net/20.500.11797/imarina6386846
    Autores:
    Venema VKCMestre OAguilar EAuer IGuijarro JADomonkos PVertacnik GSzentimrey TStepanek PZahradnicek PViarre JMüller-Westermeier GLakatos MWilliams CNMenne MJLindau RRasol DRustemeier EKolokythas KMarinova TAndresen LAcquaotta FFratianni SCheval SKlancar MBrunetti MGruber CProhom Duran MLikso TEsteban PBrandsma T
    Resumen:
    The COST (European Cooperation in Science and Technology) Action ES0601: advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random independent break-type inhomogeneities with normally distributed breakpoint sizes were added to the simulated datasets. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added. Participants provided 25 separate homogenized contributions as part of the blind study. After the deadline at which details of the imposed inhomogeneities were revealed, 22 additional solutions were submitted. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency

  • Otros:

    Autor según el artículo: Venema VKC; Mestre O; Aguilar E; Auer I; Guijarro JA; Domonkos P; Vertacnik G; Szentimrey T; Stepanek P; Zahradnicek P; Viarre J; Müller-Westermeier G; Lakatos M; Williams CN; Menne MJ; Lindau R; Rasol D; Rustemeier E; Kolokythas K; Marinova T; Andresen L; Acquaotta F; Fratianni S; Cheval S; Klancar M; Brunetti M; Gruber C; Prohom Duran M; Likso T; Esteban P; Brandsma T
    Departamento: Geografia
    Autor/es de la URV: Aguilar Anfrons, Enrique Modesto / DOMONKOS, PÉTER
    Palabras clave: United-states Time-series Temperature data Surrogate data Statistical characteristics Precipitation series Inhomogeneities Homogeneity test Discontinuities Climate data
    Resumen: The COST (European Cooperation in Science and Technology) Action ES0601: advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random independent break-type inhomogeneities with normally distributed breakpoint sizes were added to the simulated datasets. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added. Participants provided 25 separate homogenized contributions as part of the blind study. After the deadline at which details of the imposed inhomogeneities were revealed, 22 additional solutions were submitted. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training the users on homogenization software was found to be very important. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that automatic algorithms can perform as well as manual ones. © Author(s) 2012.
    Áreas temáticas: Stratigraphy Paleontology Meteorology & atmospheric sciences Global and planetary change Geosciences, multidisciplinary Geociências Ciências ambientais
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: enric.aguilar@urv.cat
    Identificador del autor: 0000-0002-8384-377X
    Fecha de alta del registro: 2024-06-01
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://cp.copernicus.org/articles/8/89/2012/
    Referencia al articulo segun fuente origial: Climate Of The Past. 8 (1): 89-115
    Referencia de l'ítem segons les normes APA: Venema VKC; Mestre O; Aguilar E; Auer I; Guijarro JA; Domonkos P; Vertacnik G; Szentimrey T; Stepanek P; Zahradnicek P; Viarre J; Müller-Westermeier G (2012). Benchmarking homogenization algorithms for monthly data. Climate Of The Past, 8(1), 89-115. DOI: 10.5194/cp8892012
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.5194/cp8892012
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2012
    Tipo de publicación: Journal Publications

  • Palabras clave:

    Geosciences, Multidisciplinary,Global and Planetary Change,Meteorology & Atmospheric Sciences,Paleontology,Stratigraphy
    United-states
    Time-series
    Temperature data
    Surrogate data
    Statistical characteristics
    Precipitation series
    Inhomogeneities
    Homogeneity test
    Discontinuities
    Climate data
    Stratigraphy
    Paleontology
    Meteorology & atmospheric sciences
    Global and planetary change
    Geosciences, multidisciplinary
    Geociências
    Ciências ambientais

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