http://www.cnr.it/ontology/cnr/individuo/prodotto/ID315522

Benchmarking homogenization algorithms for monthly data (Articolo in rivista)

Type

Prodotto della ricerca (Classe)
Articolo in rivista (Classe)

Label

Benchmarking homogenization algorithms for monthly data (Articolo in rivista) (literal)

Anno

2013-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

10.1063/1.4819690 (literal)

Alternative label

Venema, Victor K C; Mestre, Olivier; Aguilar, Enric; Auer, Ingeborg; Guijarro, José A.; Domonkos, Peter; Verta?nik, Gregor; Szentimrey, Tamás; ?t?pánek, Petr; Zahradní?ek, Pavel; Viarre, J.; Müller-Westermeier, Gerhard; Lakatos, Mónika; Williams, Claude N.; Menne, Matthew J.; Lindau, Ralf; Rasol, Dubravka; Rustemeier, Elke; Kolokythas, K.; Marinova, Tania; Andresen, L.; Acquaotta, Fiorella; Fratiannil, S.; Cheval, Sorin; Klan?ar, Matija; Brunetti, Michele; Gruber, Christian; Prohom Duran, M.; Likso, Tanja; Esteban, Pesteban; Brandsma, Theo; Willett, Katharine M. (2013)
Benchmarking homogenization algorithms for monthly data
in AIP conference proceedings
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Venema, Victor K C; Mestre, Olivier; Aguilar, Enric; Auer, Ingeborg; Guijarro, José A.; Domonkos, Peter; Verta?nik, Gregor; Szentimrey, Tamás; ?t?pánek, Petr; Zahradní?ek, Pavel; Viarre, J.; Müller-Westermeier, Gerhard; Lakatos, Mónika; Williams, Claude N.; Menne, Matthew J.; Lindau, Ralf; Rasol, Dubravka; Rustemeier, Elke; Kolokythas, K.; Marinova, Tania; Andresen, L.; Acquaotta, Fiorella; Fratiannil, S.; Cheval, Sorin; Klan?ar, Matija; Brunetti, Michele; Gruber, Christian; Prohom Duran, M.; Likso, Tanja; Esteban, Pesteban; Brandsma, Theo; Willett, Katharine M. (literal)

Pagina inizio

1060 (literal)

Pagina fine

1065 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

http://www.scopus.com/record/display.url?eid=2-s2.0-84885008976&origin=inward (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

1552 8 (literal)

Rivista

AIP conference proceedings (Rivista)

Note

Scopu (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

Universitat Bonn; Ecole Nationale de la Météorologie; Universitat Rovira i Virgili; Central Institute for Meteorology and Geodynamics, Vienna; Agencia Estatal de Meteorologia; Slovenian Environment Agency; Hungarian Meteorological Service; Czech Hydrometeorological Institute; Ustavu systemove biologie a ekologie; Deutscher Wetterdienst; NOAA National Climatic Data Center; Meteorological and Hydrological Service; Panepistimion Patron; National Institute of Meteorology and Hydrology Bulgarian Academy of Sciences; Meteorologisk institutt; Universita degli Studi di Torino; National Meteorological Administration; National Institute for RandD in Environmental Protection; Institute of Atmospheric Sciences and Climate, Bologna; Universitat de Barcelona; Meteorological Service of Catalonia; Centre d'Estudis de la Neu i de la Muntanya d'Andorra (CENMA-IEA) Andorra; Royal Netherlands Meteorological Institute; Met Office (literal)

Titolo

Benchmarking homogenization algorithms for monthly data (literal)

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. © 2013 AIP Publishing LLC. (literal)

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