Validation metrics of homogenization techniques on artificially inhomogenized monthly temperature networks in Sweden and Slovenia (1950–2005)

Coscarelli, Roberto; Caroletti, Giulio Nils; Joelsson, Magnus; Engström, Erik; Caloiero, Tommaso

doi:10.1038/s41598-021-97685-7

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Other metrics commonly used in homogenization studies have been the centred root mean squared error (CRMSE; Gubler et al, 2017; Joelsson et al, 2022; Killick et al, 2022) and the Pearson correlation coefficient (Coscarelli et al, 2021) between the homogenized and the problem series. Both have been disregarded here because they can be misleading, since when a series is added to or multiplied by a big number it would still show a good result in terms of CRMSE or correlation coefficient, respectively.…”

Section: Discussionmentioning

confidence: 99%

Homogenization of monthly series of temperature and precipitation: Benchmarking results of the MULTITEST project

Guijarro

López

Aguilar

et al. 2023

Intl Journal of Climatology

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The homogenization of climate observational series is a needed process before undertaking confidently any study of their internal variability, since changes in the observation methods or in the surroundings of the observatories, for instance, can introduce biases in the data of the same order of magnitude than the underlying climate variations and trends. Many methods have been proposed in the past to remove the unwanted perturbations from the climatic series, and some of them have been implemented in software packages freely available on the Internet. The Spanish project MULTITEST was intended to test their performance in an automatic way with synthetic monthly series of air temperature and atmospheric precipitation, to update inter-comparison results from former projects, especially those of the COST Action ES0601. Several networks representing different climates and station densities were used to test a variety of homogenization packages on hundreds of random samples. Results were evaluated mainly in the form of root mean squared errors and errors in the trend of the series, showing that ACMANT, followed by Climatol, minimized these errors. However, other packages performed also relatively well, even outperforming them when there were simultaneous biases of the same sign in most or all the test series.

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Section: Discussionmentioning

confidence: 99%

Homogenization of monthly series of temperature and precipitation: Benchmarking results of the MULTITEST project

Guijarro

López

Aguilar

et al. 2023

Intl Journal of Climatology

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“…HOMER is a semi‐automatic tool, but has recently been develop into a fully automatic tool called Bart (Joelsson et al ., 2022). The performance of Bart has been evaluated on synthetic benchmark data (Coscarelli et al ., 2021; Joelsson et al ., 2022).…”

Section: Introductionmentioning

confidence: 99%

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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Homogenised Monthly and Daily Temperature and Precipitation Time Series in China and Greece since 1960

Argiriou

Armaos

et al. 2023

Adv. Atmos. Sci.

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In this paper, we describe and analyze two datasets entitled “Homogenised monthly and daily temperature and precipitation time series in China during 1960–2021” and “Homogenised monthly and daily temperature and precipitation time series in Greece during 1960–2010”. These datasets provide the homogenised monthly and daily mean (TG), minimum (TN), and maximum (TX) temperature and precipitation (RR) records since 1960 at 366 stations in China and 56 stations in Greece. The datasets are available at the Science Data Bank repository and can be downloaded from https://doi.org/10.57760/sciencedb.01731 and https://doi.org/10.57760/sciencedb.01720. For China, the regional mean annual TG, TX, TN, and RR series during 1960–2021 showed significant warming or increasing trends of 0.27°C (10 yr)−1, 0.22°C (10 yr)−1, 0.35°C (10 yr)−1, and 6.81 mm (10 yr)−1, respectively. Most of the seasonal series revealed trends significant at the 0.05 level, except for the spring, summer, and autumn RR series. For Greece, there were increasing trends of 0.09°C (10 yr)−1, 0.08°C (10 yr)−1, and 0.11°C (10 yr)−1 for the annual TG, TX, and TN series, respectively, while a decreasing trend of −23.35 mm (10 yr)−1 was present for RR. The seasonal trends showed a significant warming rate for summer, but no significant changes were noted for spring (except for TN), autumn, and winter. For RR, only the winter time series displayed a statistically significant and robust trend [−15.82 mm (10 yr)−1]. The final homogenised temperature and precipitation time series for both China and Greece provide a better representation of the large-scale pattern of climate change over the past decades and provide a quality information source for climatological analyses.

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Validation metrics of homogenization techniques on artificially inhomogenized monthly temperature networks in Sweden and Slovenia (1950–2005)

Cited by 4 publications

References 26 publications

Homogenization of monthly series of temperature and precipitation: Benchmarking results of the MULTITEST project

Homogenization of monthly series of temperature and precipitation: Benchmarking results of the MULTITEST project

Homogenization of Swedish mean monthly temperature series 1860–2021

Homogenised Monthly and Daily Temperature and Precipitation Time Series in China and Greece since 1960

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