Detection and correction of artificial shifts in climate series

By Henri Caussinus, O. Mestre. 2004

314 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/c6e925f8739b86e2b9b84b088a562b4e233ec559

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Citations in the corpus

Efficiency evaluation for detecting inhomogeneities by objective homogenisation methods (2011, methodology): “The only exceptions are the Caussinus– Mestre method (C-M, Caussinus and Mestre 2004) and the Multiple Analysis of Series for Homogenisation (MASH, Szentimrey 1999) whose algorithms are capable of detecting multiple structures of IHs directly.”
Accounting for Autocorrelation in Detecting Mean Shifts in Climate Data Series Using the Penalized Maximal t or F Test (2008, methodology): “…series (e.g., Vincent et al. 2002; Menne and Williams 2005; DeGaetano 2006; Wang 2006; Wan et al. 2007; among others), and several statistical methods have been proposed for detecting “undocumented shifts” (e.g., Lund and Reeves 2002; Wang 2003, 2008; Caussinus and Mestre 2004; Wang et al. 2007).”
Homogenization of precipitation time series with ACMANT (2015, methodology): “The high performance of ANOVA has both theoretical and practical pieces of evidence (Caussinus and Mestre 2004; Domonkos et al. 2011).”
Comparison of homogeneity tests for temperature using a simulation study (2016, methodology): “We have selected several well-known homogeneity tests: • SNHT (Alexandersson, 1986), • the Buishand range test (Buishand, 1982), • the Pettitt test (Pettitt, 1979), • the Von Neumann ratio test (Von Neumann, 1941), • the KW test (Kruskal and Wallis, 1952), • the TPR • the RHTest (Wang, 2008a, 2008b)”
Multi-variable error correction of regional climate models (2013, background): “Observations are error-prone (e.g. Auer et al. 2001; Frei and Schär 1998; Schmidli et al. 2002; Caussinus and Mestre 2004; Della-Marta and Wanner 2006) and particularly wind induces errors in precipitation measurements (e.g. Nešpor and Sevruk 1999; Frei et al. 2003).”
Changepoint Detection in Periodic and Autocorrelated Time Series (2007, background): “Recent work by Caussinus and Mestre (2004) and Davis et al. (2006) are rooted in efficient algorithms to find the locations of multiple changepoints.”
Homogenization of Temperature Series via Pairwise Comparisons (2009, methodology): “Alternatively, relative homogeneity testing can be implemented via a pairwise comparison of individual climate series (Jones et al. 1986; Slonosky et al. 1999; Menne and Duchon 2001; Caussinus and Mestre 2004).”
Penalized Maximal t Test for Detecting Undocumented Mean Change in Climate Data Series (2007, background): “In the mean time, Davis et al. (2006) and Caussinus and Mestre (2004) are two prominent examples of recent studies that address directly in their statistical model the issue of detecting multiple changepoints.”
A note on the use of the standard normal homogeneity test to detect inhomogeneities in climatic time series (2011, methodology): “This procedure can be carried out using either a single reference series (e.g. Peterson and Easterling, 1994; Alexandersson and Moberg, 1997a, 1997b) or multiple reference series using highly correlated series (e.g. Caussinus and Mestre, 2004).”
Benchmarking homogenization algorithms for monthly data (2012, unknown): “The typical size of the breaks is of the same order as the climate change signal during the 20 century [3-7].”
Comparison of two homogenized datasets of daily maximum/mean/minimum temperature in China during 1960–2013 (2016, methodology): “(2012) assessed the performance of nine major homogenization methods based on a benchmark dataset of monthly temperature and precipitation series and suggested five methods with better performance: Craddock (Craddock, 1979); PRODIGE (Caussinus and Mestre, 2004); Multiple Analysis of Series for Homog”
Detection of Undocumented Changepoints Using Multiple Test Statistics and Composite Reference Series. (2005, methodology): “The nature of the jumps that are identified in a series is calibrated via a penalty function like (see also Akaike 1974; Schwarz 1978; Caussinus and Mestre 2004).”
SPLIDHOM: A Method for Homogenization of Daily Temperature Observations (2011, methodology): “3, which is roughly the standard error estimate observed on monthly adjustment coefficients computed using the Caussinus and Mestre (2004) analysis-of-variance (ANOVA) model.”
Homogenization of the Global Radiosonde Temperature Dataset through Combined Comparison with Reanalysis Background Series and Neighboring Stations (2012, methodology): “The good performance of the pairwise intercomparison methods of Caussinus and Mestre (2004) and Menne and Williams (2009) in a recent intercomparison of surface data homogenization methods (Venema et al.”
Penalized Maximal F Test for Detecting Undocumented Mean Shift without Trend Change (2008, background): “Nevertheless, readers are referred to Caussinus and Mestre (2004) and Davis et al. (2006) for statistical methods that address directly the issue of detecting multiple changepoints in different settings. Readers are also referred to Wang and Feng (2007), and Wang (2008) for algorithms for testing un”
Pairwise homogeneity assessment of HadISD (2014, methodology): “(Caussinus and Mestre , 2004) but uses a reference series rather than a pairwise comparison along with some improvements to the detection, correction and interpolation steps. HOMER was constructed to include the best characteristics of other state-of-the-art homogenisation methods after the COST-HOM”
Inference for single and multiple change‐points in time series (2013, background): “…articles based on Bayesian and related principles include those of Yao (1988), Barry and Hartigan (1992, 1993), Chib (1998), Wang and Zivot (2000), Caussinus and Mestre (2004), Fearnhead (2006), Zhao and Chu (2006), Seidou and Ouarda (2007), Giordani and Kohn (2008), Koop and Potter (2009), Chong…”