Benchmarking homogenization algorithms for monthly data

By V. Venema, O. Mestre, E. Aguilar, I. Auer, J. Guijarro, P. Domonkos, G. Vertačnik, T. Szentimrey, Petr Stepánek, P. Zahradníček and 22 more. 2012

389 citations recorded by the source corpus.

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 nor- mally distributed breakpoint sizes were added to the simu- lated datasets. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The sim- ulated time series also contained outliers, missing data peri- ods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added.

Subfields: Global and Planetary Change, Management Science and Operations Research, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/f60eab451270b3ceb2d3d59a9a572f3b1e93fff1

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In Global and Planetary Change

In Management Science and Operations Research

Citations in the corpus

Homogenization of the Global Radiosonde Temperature Dataset through Combined Comparison with Reanalysis Background Series and Neighboring Stations (2012, methodology): “A large variety of upper air data and surface data homogenization methods work with this idea (see, e.g., Thorne et al. 2005b; Sherwood et al. 2005; Venema et al. 2012).”
Comparison of homogeneity tests for temperature using a simulation study (2016, background): “Venema et al. (2012) make a comprehensive comparison of the detection and homogenization algorithms with applications on real and simulated data.”
Homogenization of precipitation time series with ACMANT (2015, methodology): “Bivariate detection was applied first in the early version of ACMANT (Venema et al. 2012), and then, it has been adapted in some other homogenization methods (Guijarro 2011; Mestre et al. 2013; Domonkos 2014).”
A call for new approaches to quantifying biases in observations of sea-surface temperature (2017, methodology): “It may also be possible to adapt methods developed for the homogenization of land station data (Venema et al. 2012).”
Homogenization of daily temperature series in the European Climate Assessment & Dataset (2018, background): “Recent comparisons (Domonkos, 2013; Lindau and Venema, 2013; Venema et al., 2013) have pointed out advantages and drawbacks of the most common systems.”