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

Guijarro, José Antonio; López, José Antonio; Aguilar, Enric; Domonkos, Péter; Venema, Victor; Sigrò, Javier; Brunet, Manola

doi:10.1002/joc.8069

Cited by 15 publications

(14 citation statements)

References 32 publications

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“…There are several reasons why the Climatol software was used to homogenize the daily time series of the essential climate variables of Ukraine: (1) it is capable to perform homogenization of data with a daily time resolution (e.g., Azorin‐Molina et al, 2019); (2) the software has been well evaluated and verified along with other similar programs/algorithms and it has shown good results (Guijarro et al, 2019; Venema et al, 2012); (3) it can be applied automatically what significantly facilitates homogenization of large datasets; (4) uncertainties of the Climatol adjustment algorithm have been evaluated and quantified what can provide an assessment of the added value of the Climatol homogenization (Skrynyk et al, 2020); (5) it has lower (compared to other similar software) requirements for the completeness of the time series and allows to perform their homogenization even in case of a relatively large number of missing data.…”

Section: Methodsmentioning

confidence: 99%

Data quality control and homogenization of daily precipitation and air temperature (mean, max and min) time series of Ukraine

Skrynyk

Sidenko

Aguilar

et al. 2023

Intl Journal of Climatology

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In this paper we present the results of quality control and homogenization procedures applied to long time series of daily atmospheric precipitation sums (Rr) and daily mean (Tm), maximum (Tx) and minimum (Tn) air temperature collected in Ukraine. The daily data from 178 meteorological stations covering the period of 1946–2020 were analysed. In order to perform a thorough quality assurance check, we used the R package INQC, while the Climatol homogenization software was used to detect and remove breaks from the time series. The INQC quality assurance tests revealed a relatively small number of erroneous records (around 0.01% for each variable) and suspicious values (up to 0.09%). The application of Climatol resulted in 195, 296, 355 and 359 break points, detected for Rr, Tm, Tx and Tn, respectively. These quantities coincide roughly with the results of the HOMER homogenization procedure applied to monthly time series for the same stations and almost the same period (performed in the previous works of the authors). To verify the homogenization results, statistical comparison of the raw and homogenized time series was performed. The verification demonstrated that the quality control and homogenization procedures detected and removed errors and breaks very well, and air temperature and precipitation fields after the homogenization are more self‐consistent compared to the original raw data.

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Section: Methodsmentioning

confidence: 99%

Data quality control and homogenization of daily precipitation and air temperature (mean, max and min) time series of Ukraine

Skrynyk

Sidenko

Aguilar

et al. 2023

Intl Journal of Climatology

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“…Alternative method include the use of homogenisation tools (Guijarro et al, 2023;Guijarro, 2024), or AI methods (Kadow et al, 2020).…”

Section: Gridclim Gridded Data-setmentioning

confidence: 99%

“…An a posteriori correction can be performed using homogenisation, such as BaRT/Homer (Joelsson et al, 2023) or Climatol (Guijarro et al, 2023), both homogenisation tools being used at SMHI. Homogenisation is particularly recommend when working with coupled records, since the 'stitching' is likely to introduce variations that are not related to climate, hence considered inhomogeneities.…”

Section: Added Value Of Station Coupling and Homogenisationmentioning

confidence: 99%

Spatially aggregated climate indicators over Sweden (1860–2020), Part 1: Temperature

Sturm

2024

Preprint

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Abstract. Climate indicators are useful tools to synthesise climate information from multiple station time-series into a single national indicator. The method applied should be spatially representative and robust over time. We introduce a new method, based on Empirical Orthogonal Functions (EOF) during the calibration period 1961–2018, in order to reconstruct the climate indicator for temperature in Sweden for the full 1860–2020 period of available observations. The new method delivers results in good overall agreement with the reference method (i.e. arithmetic mean from selected stations in the reference network). Discrepancies are found prior to 1900, primarily related to the reduced number of active stations: the robustness of the indicator estimation is assessed by an ensemble computation with added random noise, which confirms that the ensemble spread increases significantly prior to 1880. The present study establishes that the 10-year running averaged temperature indicator rose from −1.03 °C in 1903 to +1.19 °C in 2010 (with respect to a mean value of 4.64 °C over the 1961–2018 calibration period), i.e. an increase by +2.22 °C in a century. The temperature difference between 1860 and 2020 was largest for winter (DJF) averages (+3 °C) and minimal for summer (+2 °C). The leading EOF patterns illustrate the spatial modes of variability for climate variability, with a predominantly homogeneous, mono-modal distribution for temperature. For precipitation, the first EOF pattern displays more pronounced regional features (maximum over the West coast), which is completed by a north-south seesaw pattern for the second EOF. We illustrate that EOF patterns calculated from observation data reproduce the major features of EOF calculated from GridClim, a gridded data-set over Sweden, for annual and seasonal averages. The leading EOF patterns vary significantly for seasonal averages (DJF, MAM, JJA, SON) for temperature. Finally, future developments of the EOF-method are discussed for calculating regional aggregated climate indicators, their relationship to synoptic circulation patterns and the benefits of homogenisation of observation series. The EOF-based method to compute a spatially aggregated indicator for precipitation is presented in a companion article (Sturm, 2024a). The code and data for this study is available on Zenodo (Sturm, 2024b).

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“…The most recent version ACMANTv5.1 is usable for the homogenization of several climate variables either in daily or monthly time resolution. The efficiency of automatic ACMANTv4 was extensively tested by the Spanish MULTITEST project, and there ACMANT was found to be generally more accurate than any other tested method (Domonkos et al, 2021; Guijarro et al, 2017).…”

Section: Homogenizationmentioning

confidence: 99%

CADTEP: A new daily quality‐controlled and homogenized climate database for Catalonia (1950–2021)

Prohom

Domonkos

Cunillera

et al. 2023

Intl Journal of Climatology

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New daily minimum temperature (TN), maximum temperature (TX) and precipitation amount (PPT) database CADTEP (CAtalan Daily TEmperature and Precipitation data set) has been developed for Catalonia, Spain. The source dataset consists of the available climate records from Catalonia provided by the State Meteorology Agency of Spain (AEMET) and the Catalan Meteorological Service (SMC). We selected the long and fairly complete records and created further long series by merging the records of nearby stations. Finally, 26 TN, 26 TX and 72 PPT time series covering the period 1950–2021 have been accepted for CADTEP from the several hundreds of, but mostly much shorter time series. This database has sufficiently dense time series to reach accurate estimates of regional climate variability. CADTEP time series were subjected to thorough quality control and time series homogenization. In the quality control, many shorter series were still in use. Both in quality control and time series homogenization, several series surrounding Catalonia, but close to the borders, were also considered to achieve a higher data accuracy in CADTEP. The homogenization was performed with ACMANTv5.1. In this new version of ACMANT, metadata dates pointing on likely occurrences of nonclimatic changes (breaks) are contemplated in the homogenization as sure break positions. Finally, we examined the trend and variability of TN, TX and PPT over 1950–2021 using CADTEP. Differences between the use of homogenized and raw data, as well as between the inclusion and exclusion of metadata use in the homogenization procedure were controlled. The most important conclusions are related to the trend analysis, namely the speed of the warming of summer TX is faster than in other seasons, faster than the warming of TN in general, and the triple of the mean global warming speed.

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

Cited by 15 publications

References 32 publications

Data quality control and homogenization of daily precipitation and air temperature (mean, max and min) time series of Ukraine

Data quality control and homogenization of daily precipitation and air temperature (mean, max and min) time series of Ukraine

Spatially aggregated climate indicators over Sweden (1860–2020), Part 1: Temperature

CADTEP: A new daily quality‐controlled and homogenized climate database for Catalonia (1950–2021)

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