Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11765/1491
Benchmarking homogenization algorithms for monthly data
Title: Benchmarking homogenization algorithms for monthly data
Authors: Venema, Victor K. C.Mestre, OlivierAguilar, EnricAuer, IngeborgGuijarro, José Antonio ORCID RESEARCHERID Autor AEMETDomonkos, 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.
Keywords: Surface climate network; Instrumental climate records; Monthly temperature records; Monthly precipitation records; Homogenization
Issue Date: 2013
Publisher: American Institute of Physics
Citation: AIP Conference Proceedings. 2013, 1552, p. 1060-1065
Publisher version: http://dx.doi.org/10.1063/1.4819690
Abstract: 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
1551-7616
Appears in Collections:Artículos científicos 2010-2014


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