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www.clim-past-discuss.net/7/2655/2011/
doi:10.5194/cpd-7-2655-2011
© Author(s) 2011. This work is distributed
under the Creative Commons Attribution 3.0 License.
Benchmarking monthly homogenization algorithms
1Meteorological institute of the University of Bonn, Germany
2Meteo France, Ecole Nationale de la Meteorologie, Toulouse, France
3Center on Climate Change (C3), Tarragona, Spain
4Zentralanstalt für Meteorologie und Geodynamik, Wien, Austria
5Agencia Estatal de Meteorologia, Palma de Mallorca, Spain
6Slovenian Environment Agency, Ljubljana, Slovenia
7Hungarian Meteorological Service, Budapest, Hungary
8Czech Hydrometeorological Institute, Brno, Czech Republic
9Deutscher Wetterdienst, Offenbach, Germany
10NOAA/National Climatic Data Center, USA
11Meteorological and hydrological service, Zagreb, Croatia
12Laboratory of Atmospheric Physics, University of Patras, Greece
13National Institute of Meteorology and Hydrology – BAS, Sofia, Bulgaria
14Norwegian Meteorological Institute, Oslo, Norway
15Department of Earth Science, University of Turin, Italy
16National Meteorological Administration, Bucharest, Romania
17National Institute for R&D in Environmental Protection, Bucharest, Romania
18Institute of Atmospheric Sciences and Climate (ISAC-CNR), Bologna, Italy
19Grup de Climatologia, Universitat de Barcelona, Spain
20Meteorological Service of Catalonia, Area of Climatology, Barcelona, Catalonia, Spain
21Centre d'Estudis de la Neu i de la Muntanya d'Andorra (CENMA-IEA), Andorra
22Royal Netherlands Meteorological Institute, De Bilt, The Netherlands
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 break-type inhomogeneities were added to the simulated datasets modeled as a Poisson process with normally distributed breakpoint sizes. 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 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 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 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 currently automatic algorithms can perform as well as manual ones.
Discussion Paper (PDF, 973 KB) Interactive Discussion (Closed, 6 Comments) Final Revised Paper (CP)
Notice on Discussion StatusCitation: Venema, V. K. C., Mestre, O., Aguilar, E., Auer, I., Guijarro, J. A., Domonkos, P., Vertacnik, G., Szentimrey, T., Stepanek, P., Zahradnicek, P., Viarre, J., Müller-Westermeier, G., Lakatos, M., Williams, C. N., Menne, M., Lindau, R., Rasol, D., Rustemeier, E., Kolokythas, K., Marinova, T., Andresen, L., Acquaotta, F., Fratianni, S., Cheval, S., Klancar, M., Brunetti, M., Gruber, C., Prohom Duran, M., Likso, T., Esteban, P., and Brandsma, T.: Benchmarking monthly homogenization algorithms, Clim. Past Discuss., 7, 2655-2718, doi:10.5194/cpd-7-2655-2011, 2011. Bibtex EndNote Reference Manager XML
