Homogenization of Temperature Series via Pairwise Comparisons

By M. Menne, C. Naomie Williams. 2009

322 citations recorded by the source corpus.

Subfields: Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/f7765d83f04d514ff79d08e01d7661e3c2be2519

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In Global and Planetary Change

1. P. Domonkos
2. L. Haimberger
3. S. Sperka
4. Christina Tavolato
5. J. Hansen
6. Yani Zhu
7. Zhen Li
8. Zhongwei Yan

Citations in the corpus

• Comparison of two homogenized datasets of daily maximum/mean/minimum temperature in China during 1960–2013 (2016, methodology): “Venema et al. (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 Se”
• Improvements in the GISTEMP Uncertainty Model (2019, methodology): “…for land stations into two independent components: the parametric uncertainty associated with the Pairwise Homogenization Algorithm (PHA; Menne & Williams, 2009) used to homogenize the GHCNv4 monthly data and incomplete homogenization caused by artificial shifts in the data that remain…”
• Efficiency evaluation for detecting inhomogeneities by objective homogenisation methods (2011, methodology): “We note that an examination of a Moravian temperature dataset resulted in very similar statistical properties of detected IHs ( Domonkos and Stěpanek 2009 ) despite large differences in the spatial density and correlation between station series.”
• Improvements in the uncertainty model in the Goddard Institute for Space Studies Surface Temperature (GISTEMP) analysis (2019, methodology): “The PHA detects artificial time series mean shifts due to changes in observing prac473 tice by comparing a station series with neighboring stations [Menne and Williams, 2009].”
• Berkeley Earth Temperature Averaging Process (2013, background): “A previous analysis of the 1218 stations in US Historical Climatology Network found that on average each record has one spurious shift in mean level greater than about 0.5 C for every 15-20 years of record (Menne et al. 2009).”
• Homogenization of the Global Radiosonde Temperature Dataset through Combined Comparison with Reanalysis Background Series and Neighboring Stations (2012, methodology): “This idea is consistent with the concept of pairwise intercomparison together with using only homogeneous subperiods for estimating break sizes (Della-Marta and Wanner 2006; Sperka 2007; Caussinus and Mestre 2004; Menne and Williams 2009).”
• Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set (2012, methodology): “Moberg et al. [2003] found evidence of bias seasonality in Swedish station records. Böhm et al. [2010] derived bias adjustments for the Greater Alpine Region prior to 1870.”
• Pairwise homogeneity assessment of HadISD (2014, methodology): “However, the requirement of an automated system which would work reliably on all 6103 HadISD stations limited this study to using PHA fromMenne et al.(2009) and Menne and Williams Jr(2009). PHA has been used on NCDC’s US Historical Climatology Network (USHCN) monthly surface temperature record, and ”
• Space‐time modelling of trends in temperature series (2011, background): “The series have not been homogenized (e.g. Menne and Williams, 2009, Lund et al., 2007), but have undergone quality control.”