Benchmarking homogenization algorithms for monthly data

作者：Koen Venema, Olivier Mestre, Enric Aguilar, Ingeborg Auer, J. A. Guijarro, Péter Domonkos, Gregor Vertačnik, Tamás Szentimrey, P. Stepanek, Pavel Zahradníček, Julien Viarre, G. Müller‐Westermeier, Mónika Lakatos, Claude N. Williams, Matthew J. Menne, Ralf Lindau, Dubravka Rasol, Elke Rustemeier, K. Kolokythas, Tania Marinova, Louise C. Andresen, Fiorella Acquaotta, Simona Fratianni, Sorin Cheval, M. Klancar, Michele Brunetti, Christine Gruber, Marc Prohom, Tanja Likso, Pere Esteban, T. Brandsma · 发表于：Climate of the past · 年份：2012 · DOI：10.5194/cp-8-89-2012 · 被引用次数：371 · 研究领域：Climate variability and models、Hydrology and Drought Analysis、Complex Systems and Time Series Analysis

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 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 scale...