Evaluation of the Homogenization Adjustments Applied to European Temperature Records in the Global Historical Climatology Network Dataset

O’Neill, Peter; Connolly, Ronan; Connolly, Michael; Soon, Willie; Chimani, Barbara; Crok, Marcel; Vos, R. de; Harde, H.; Kajaba, Peter; Nojarov, Peter; Przybylak, Rajmund; Rasol, Dubravka; Skrynyk, Oleg; Skrynyk, Olesya; Štěpánek, Petr; Wypych, Agnieszka; Zahradníček, Pavel

doi:10.3390/atmos13020285

Cited by 13 publications

(13 citation statements)

References 89 publications

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“…Inconsistency of homogenization is discussed in O'Neill et al . (2022), where the inclusion of meta data in homogenization methods is encouraged. Meta data is currently an input to the Bart tool used here, but much of the weather station inspection protocols remains to be digitized.…”

Section: Discussionmentioning

confidence: 99%

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Homogenization of Swedish mean monthly temperature series 1860–2021

Joelsson

Engström

Kjellström

2022

Intl Journal of Climatology

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The Swedish monthly average 2 m temperature observational data set is homogenized with a new automated version of the homogenization tool HOMogenizaton softwarE in R. Data from 836 individual time series (1860-2021) are merged into 456 time series with a novel automatic merging method. Merging limits the need of interpolation of data and increase the number of long time series without a net loss of data. Twenty-two of the merged time series were found to be homogeneous. For the other time series, the median time per break-point is 17 years. 37% of the detected break-points are supported in meta data. 42% of the data are corrected by ±0.5 C or less, 3% by ±1 C or more. On average corrections are negative, larger in the early periods, and larger in the summertime. The average trend 1860-2021 in the resulting merged and homogenized data set is (0.13 ± 0.03) C decade, which does not significantly differ from that of the raw observational data. Extremely warm months, defined as being outside of three times the standard deviation from the average of the full time series, are most frequent and extremely cold months least frequent in the most recent 30-year period (1991-2020). In the homogenized data set, extremely warm months are even more frequent and extremely cold months even less frequent in 1991-2020, than in the raw observational data set.climate, HOMER, homogenization, merging time series, temperature | INTRODUCTIONClimatological studies require time series data that are both homogeneous and that covers a sufficiently long time period (Venema et al., 2020). Long observational records often have artificial shifts, for example due to changes in location (e.g., Dienst et al., 2017), measurement equipment (e.g., Auchmann and Brönnimann, 2012), or land-use (e.g., Sun et al., 2016. The homogeneity of observational data must therefore be tested, and if required, data need to be homogenized. Several statistical tools are developed for this purpose (Ribeiro et al., 2016;Venema et al., 2020).At the Swedish Meteorological and Hydrological Institute (SMHI), 35 monthly average 2 m temperature time series were homogenized in 2011 using Standard Normal Homogeneity Test (SNHT,

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Section: Discussionmentioning

confidence: 99%

“…The fraction of detected break‐points supported by meta data is similar to the results of O'Neill et al . (2022), although the time windows when break‐points are considered to be supported are not identical.…”

Section: Discussionmentioning

confidence: 99%

Homogenization of Swedish mean monthly temperature series 1860–2021

Joelsson

Engström

Kjellström

2022

Intl Journal of Climatology

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“…The simplest particularisation strategy is based on binning. In meteorology, the pairwise homogenisation algorithm uses statistical analysis to dynamically define the bins 3 . In fracture mechanics, binning‐based particularisation is implemented by placing bins at the Gauss quadrature points 4 .…”

Section: Introductionmentioning

confidence: 99%

“…In meteorology, the pairwise homogenisation algorithm uses statistical analysis to dynamically define the bins. 3 In fracture mechanics, binning-based particularisation is implemented by placing bins at the Gauss quadrature points. 4 Binning particularisation is also used successfully in the reconstruction of genomes, 5 but to authors' knowledge, it has never been proposed for computational oncology problems.…”

Section: Introductionmentioning

confidence: 99%

Effect of particularisation size on the accuracy and efficiency of a multiscale tumours' growth model

Vinicius

Quintela

Kasztelnik

et al. 2022

Numer Methods Biomed Eng

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In silico, medicine models are frequently used to represent a phenomenon across multiples space-time scales. Most of these multiscale models require impracticable execution times to be solved, even using high performance computing systems, because typically each representative volume element in the upper scale model is coupled to an instance of the lower scale model; this causes a combinatory explosion of the computational cost, which increases exponentially as the number of scales to be modelled increases. To attenuate this problem, it is a common practice to interpose between the two models a particularisation operator, which maps the upper-scale model results into a smaller number of lower-scale models, and an operator, which maps the fewer results of the lower-scale models on the whole space-time homogenisation domain of upper-scale model. The aim of this study is to explore what is the simplest particularisation / homogenisation scheme that can couple a model aimed to predict the growth of a whole solid tumour (neuroblastoma) to a tissue-scale model of the cell-tissue biology with an acceptable approximation error and a viable computational cost. Using an idealised initial dataset with spatial gradients representative of those of real neuroblastomas, but small enough to be solved without any particularisation, we determined the approximation error and the computational cost of a very simple particularisation strategy based on binning. We found that even such simple algorithm can significantly reduce the computational cost with negligible approximation errors.

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“…In the fifth paper [5], we turn to the testing of homogenization methods. O'Neill et al compared different homogenization results for the same European station series obtained by the repeated performance of Pairwise Homogenization Algorithm during the development of the Global Historical Climatology Network (GHCN) dataset.…”

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confidence: 99%