Homogenisation of temperature and precipitation time series with <scp>ACMANT3</scp>: method description and efficiency tests

Domonkos, Péter; Coll, John C.

doi:10.1002/joc.4822

Cited by 49 publications

(64 citation statements)

References 32 publications

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“…A log transformation was applied to the monthly precipitation amounts. The three main methods we have implemented were as follows. ACMANT (Domonkos, 2011; Domonkos and Coll, ): The Adapted Caussinus‐Mestre Algorithm for Networks of Temperature. This method is based on a bivariate detection of changes that include a penalty term (Caussinus and Mestre, ).…”

Section: Study Area and Methodologymentioning

confidence: 99%

Changes in extreme temperature and precipitation indices: Using an innovative daily homogenized database in Israel

Yosef

Aguilar

Alpert

2019

Intl Journal of Climatology

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This study examines the 1950–2017 temporal changes in climate extremes in Israel, which is located in the East Mediterranean (EM), a region which suffers from a scarcity of long and reliable datasets. It is well known that most long‐term records are affected by artificial shifts most commonly caused by station relocation, instrumental modification and local environmental changes. Therefore, for the first time, a thorough homogenization (detection and correction) routine was developed and implemented in the long‐term records. Consequently, a new daily adjusted dataset has been generated, including 34 temperature stations and 60 precipitation stations. Based on this comprehensive dataset, 38 extreme indices recommended by the Expert Team on Climate Change Detection and the Expert Team on Sector‐specific Climate Indices have been calculated. These indices will help various sectors to plan properly mitigation actions and adaptation for climate change, in addition to facilitating future studies for the EM. The results showed highly significant changes in temperature extremes associated with warming, especially for those indices derived from the daily minimum temperature (TN, 1950–2017), whereas the maximum temperature (TX) exhibited a similar increasing magnitude of the TN (~0.55°C/decade) in the last 30 years. The warming trends, which are non‐monotonic, seem to have been particularly strong since the early 1990s. The coastal area is characterized by higher heat stress during the nighttime, while mountains exhibit a strong tendency towards increasing temperatures during the noon hours. A reduction in the total precipitation amount and in the number of wet days with a tendency towards more intense wet days was found. Although all the regional trends of the precipitation indices were not statistically significant (p ≤ .05), they showed a fine spatial coherence.

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Section: Study Area and Methodologymentioning

confidence: 99%

Changes in extreme temperature and precipitation indices: Using an innovative daily homogenized database in Israel

Yosef

Aguilar

Alpert

2019

Intl Journal of Climatology

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“…Furthermore, the input data for ACMANT3 (homogenization method used in the present study; see Sect. 3.4.2) should not include estimated data (Domonkos and Coll, 2017). Hence, in order to avoid introducing uncertainty by filling gaps, no data were estimated for the present study.…”

Section: Data Quality Issue Descriptionmentioning

confidence: 99%

“…For the break detection and adjustment method used in this study (Sect. 3.4.2), the optimal cluster size is usually around 20 to 30 stations, but the optimal number of stations can be much higher if record lengths and data completeness differ between the time series (Domonkos and Coll, 2017). This strongly applies to the Central Andean data.…”

Section: Data Homogenization 341 Clusteringmentioning

confidence: 99%

“…ACMANT is a state-of-the-art homogenization method with one of the best performances (Ribeiro et al, 2016;Venema et al, 2012). Recently, a new version of the approach (ACMANT3) was published (Domonkos and Coll, 2017). Compared to previous versions (Domonkos, 2011(Domonkos, , 2015, the performance of the method was further improved and the range of use increased (Domonkos and Coll, 2017).…”

Section: Break-point Detection and Adjustmentmentioning

confidence: 99%

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Effects of undetected data quality issues on climatological analyses

et al. 2018

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Abstract. Systematic data quality issues may occur at various stages of the data generation process. They may affect large fractions of observational datasets and remain largely undetected with standard data quality control. This study investigates the effects of such undetected data quality issues on the results of climatological analyses. For this purpose, we quality controlled daily observations of manned weather stations from the Central Andean area with a standard and an enhanced approach. The climate variables analysed are minimum and maximum temperature and precipitation. About 40 % of the observations are inappropriate for the calculation of monthly temperature means and precipitation sums due to data quality issues. These quality problems undetected with the standard quality control approach strongly affect climatological analyses, since they reduce the correlation coefficients of station pairs, deteriorate the performance of data homogenization methods, increase the spread of individual station trends, and significantly bias regional temperature trends. Our findings indicate that undetected data quality issues are included in important and frequently used observational datasets and hence may affect a high number of climatological studies. It is of utmost importance to apply comprehensive and adequate data quality control approaches on manned weather station records in order to avoid biased results and large uncertainties.

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“…Ribeiro et al (2016a) describe their main characteristics, namely of ACMANT and its units ACMANT2 (Domonkos, ), Climatol (Guijarro, ), RHTest (Wang, ), AnClim and ProClimDB (Štepánek, , ), and HOMER (Mestre et al , ). More recently, the ACMANT3 unit has been released (Domonkos and Coll, ). Some of the methods recommended by the HOME project are available in HOMER for monthly data, and HOM/SPLIDHOM for daily data (Mestre et al , ).…”

Section: Introductionmentioning

confidence: 99%

gsimcli: a geostatistical procedure for the homogenisation of climatic time series

Ribeiro

Caineta

Costa

et al. 2016

Intl Journal of Climatology

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Climate data homogenisation is of major importance in monitoring climate change and in validating weather forecasts, general circulation and regional atmospheric models, modelling of erosion and drought monitoring, among other impact studies. Discontinuities in the time series, also named inhomogeneities, may lead to biased conclusions in such studies, so they should be detected and corrected. Previous studies have suggested a geostatistical stochastic approach, which uses Direct Sequential Simulation (DSS), as a promising methodology for the homogenisation of precipitation data series. Based on the spatial and temporal correlation between the neighbouring stations, DSS calculates local probability density functions at a candidate station to detect inhomogeneities. Here, we present a new method named gsimcli (Geostatistical SIMulation for the homogenisation of CLImate data), which is an improved and extended version of that approach. This technique is novel in its incorporation of spatial correlation metrics for the homogenisation of climate time series. The method's performance is assessed with annual and monthly precipitation, and monthly temperature data from two regions of the COST‐HOME benchmark data set, and the results are compared using performance metrics. We also evaluate a semi‐automatic version of the gsimcli method, which performs additional adjustments for sudden shifts. Both gsimcli versions provided similar results in the homogenisation of annual series. The gsimcli method was more efficient in the homogenisation of the benchmark's precipitation series than the original geostatistical approach. The gsimcli approach performed more closely to state‐of‐the‐art procedures in the homogenisation of monthly data than in the homogenisation of annual data. We expect that the proposed procedure will open new perspectives for the development of techniques that detect and correct inhomogeneities in climate data with monthly and sub‐monthly resolution.

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Homogenisation of temperature and precipitation time series with ACMANT3: method description and efficiency tests

Cited by 49 publications

References 32 publications

Changes in extreme temperature and precipitation indices: Using an innovative daily homogenized database in Israel

Changes in extreme temperature and precipitation indices: Using an innovative daily homogenized database in Israel

Effects of undetected data quality issues on climatological analyses

gsimcli: a geostatistical procedure for the homogenisation of climatic time series

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