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| Titel |
Benchmarking homogenization algorithms for monthly data |
| VerfasserIn |
V. K. C. Venema, O. Mestre, E. Aguilar, I. Auer, J. A. Guijarro, P. Domonkos, G. Vertacnik, T. Szentimrey, P. Stepanek, P. Zahradnicek, J. Viarre, G. Müller-Westermeier, M. Lakatos, C. N. Williams, M. J. Menne, R. Lindau, D. Rasol, E. Rustemeier, K. Kolokythas, T. Marinova, L. Andresen, F. Acquaotta, S. Fratianni, S. Cheval, M. Klancar, M. Brunetti, C. Gruber, M. Prohom Duran, T. Likso, P. Esteban, T. Brandsma |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1814-9324
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| Digitales Dokument |
URL |
| Erschienen |
In: Climate of the Past ; 8, no. 1 ; Nr. 8, no. 1 (2012-01-10), S.89-115 |
| Datensatznummer |
250005363
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| Publikation (Nr.) |
 copernicus.org/cp-8-89-2012.pdf |
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| Zusammenfassung |
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. |
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