HOMER : a homogenization software - methods and applications

Between 2007-2011, the European COST Action ES0601 called HOME project was devoted to evaluate the performance of homogenization methods used in climatology and produce a software that would be a synthesis of the best aspects of some of the most efficient methods. HOMER (HOMogenizaton softwarE in R) is a software for homogenizing essential climate variables at monthly and annual time scales. HOMER has been constructed exploiting the best characteristics of some other state-of-the-art homogenization methods, i.e., PRODIGE, ACMANT, CLIMATOL, and the recently developed joint-segmentation method (cghseg). HOMER is based on the methodology of optimal segmentation with dynamic programing, the application of a network-wide two-factor model both for detection and correction, and some new techniques in the coordination of detection processes from multiannual to monthly scales. HOMER also includes a tool to assess trend biases in urban temperature series (UBRIS). HOMER's approach to the final homogenization results is iterative. HOMER is an interactive method, that takes advantage of metadata. A practical application of HOMER is presented on temperature series of Wien, Austria and its surroundings.

[1]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[2]  Henri Caussinus,et al.  Detection and correction of artificial shifts in climate series , 2004 .

[3]  G. Boulet,et al.  Comparison of techniques for detection of discontinuities in temperature series , 2003 .

[4]  Marc Lavielle,et al.  Optimal segmentation of random processes , 1998, IEEE Trans. Signal Process..

[5]  Peter Domonkos,et al.  Benchmarking monthly homogenization algorithms , 2011 .

[6]  J. M. Craddock,et al.  METHODS OF COMPARING ANNUAL RAINFALL RECORDS FOR CLIMATIC PURPOSES , 1979 .

[7]  G. Drogue,et al.  Recent warming in a small region with semi-oceanic climate, 1949–1998: what is the ground truth? , 2005 .

[8]  D. Easterling,et al.  Homogeneity adjustments of in situ atmospheric climate data: a review , 1998 .

[9]  Thomas C. Peterson,et al.  Assessment of Urban versus Rural in Situ surface temperatures in the contiguous United States: No di , 2003 .

[10]  Henri Caussinus,et al.  Choosing a Linear Model with a Random Number of Change-Points and Outliers , 1997 .

[11]  Stéphane Robin,et al.  Joint segmentation, calling, and normalization of multiple CGH profiles. , 2011, Biostatistics.

[12]  Thomas C. Peterson,et al.  The minimization of the screen bias from ancient Western Mediterranean air temperature records: an exploratory statistical analysis , 2011 .

[13]  Taha B. M. J. Ouarda,et al.  Intercomparison of homogenization techniques for precipitation data , 2008 .

[14]  Chong Gu,et al.  PENALIZED LIKELIHOOD DENSITY ESTIMATION: DIRECT CROSS-VALIDATION AND SCALABLE APPROXIMATION , 2003 .

[15]  P. Massart,et al.  Gaussian model selection , 2001 .

[16]  Michele Brunetti,et al.  A new instrumental precipitation dataset for the greater alpine region for the period 1800–2002 , 2005 .

[17]  Claude N. Williams,et al.  Detection of Undocumented Changepoints Using Multiple Test Statistics and Composite Reference Series. , 2005 .

[18]  Reinhard Böhm.  Urban Bias in Temperature Time Series – a Case Study for the City of Vienna, Austria , 1998 .

[19]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[20]  Peter Domonkos,et al.  The historical pathway towards more accurate homogenisation , 2012 .

[21]  David O Siegmund,et al.  A Modified Bayes Information Criterion with Applications to the Analysis of Comparative Genomic Hybridization Data , 2007, Biometrics.

[22]  Douglas M. Hawkins,et al.  On the Choice of Segments in Piecewise Approximation , 1972 .

[23]  Peter Domonkos,et al.  Adapted Caussinus-Mestre Algorithm for Networks of Temperature series (ACMANT) , 2011 .