A Method of Homogenizing the Extremes and Mean of Daily Temperature Measurements

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SPLIDHOM: A Method for Homogenization of Daily Temperature Observations (2011, methodology): “Note that the definition of homogeneous subperiods provided in section 2a is exactly the same as that in Della-Marta and Wanner (2006). a. Notation In the following, we denote Y as the candidate series and X as the reference series.”
Comparison of two homogenized datasets of daily maximum/mean/minimum temperature in China during 1960–2013 (2016, methodology): “Coll (2010) showed that the adjusted series based on Percentiles (Štěpánek, 2009), HOM (Della-Marta and Wanner, 2006), SPLIDHOM (Mestre et al., 2011), and the Vincent method (Vincent et al., 2002) were similar, but different from those based on Quantile Matching (QM; Wang et al., 2010; Vincent et”
Homogenization of the Global Radiosonde Temperature Dataset through Combined Comparison with Reanalysis Background Series and Neighboring Stations (2012, methodology): “…daily data could be used to make more sophisticated adjustments that modify the probability distribution functions of the observations (e.g., Dai et al. 2011; Della-Marta and Wanner 2006), we restricted ourselves to modifying the mean, since this is challenging enough, as is clearly shown in Figs.”