Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/20.500.11765/1491
Benchmarking homogenization algorithms for monthly data
Título : Benchmarking homogenization algorithms for monthly data
Autor : Venema, Victor K. C.Mestre, OlivierAguilar, EnricAuer, IngeborgGuijarro, José Antonio ORCID RESEARCHERID Domonkos, PeterVertacnik, GregorSzentimrey, T.Stepanek, PetrZahradnicek, P.Viarre, J.Müller-Westermeier, G.Lakatos, MónicaWilliams, C. N.Menne, M. J.Lindau, R.Rasol, D.Rustemeier, E.Kolokythas, K.Marinova, T.Andresen, L.Acquaotta, F.Fratianni, S.Cheval, S.Klancar, MatijaBrunetti, MicheleGruber, C.Prohom, MarcLikso, T.Esteban, PereBrandsma, T.Willett, K. M.
Palabras clave : Surface climate network; Instrumental climate records; Monthly temperature records; Monthly precipitation records; Homogenization
Fecha de publicación : 2013
Editor: American Institute of Physics
Citación : AIP Conference Proceedings. 2013, 1552, p. 1060-1065
Versión del editor: http://dx.doi.org/10.1063/1.4819690
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. The algorithms were validated against a realistic benchmark dataset. Participants provided 25 separate homogenized contributions as part of the blind study as well as 22 additional solutions submitted after the details of the imposed inhomogeneities were revealed. These homogenized datasets were assessed by a number of performance metrics including i) the centered root mean square error relative to the true homogeneous values 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. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that currently automatic algorithms can perform as well as manual ones.
URI : http://hdl.handle.net/20.500.11765/1491
ISSN : 0094-243X
Colecciones: Artículos científicos 2010-2014

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