The historical pathway towards more accurate homogenisation

ABSTRACT:Relative homogenization methods assume that measurements of nearby stations experience similar climate signals and rely therefore on dense station networks with high-temporal correlations. In developing countries such as Peru, however, networks often suffer from low-station density. The aim of this study is to quantify the influence of network density on homogenization. To this end, the homogenization method HOMER was applied to an artificially thinned Swiss network.Four homogenization experiments, reflecting different homogenization approaches, were examined. Such approaches include diverse levels of interaction of the homogenization operators with HOMER, and different application of metadata. To evaluate the performance of HOMER in the sparse networks, a reference series was built by applying HOMER under the best possible conditions. Applied in completely automatic mode, HOMER decreases the reliability of temperature records. Therefore, automatic use of HOMER is not recommended. If HOMER is applied in interactive mode, the reliability of temperature and precipitation data may be increased in sparse networks. However, breakpoints must be inserted conservatively. Information from metadata should be used only to determine the exact timing of statistically detected breaks. Insertion of additional breakpoints based solely on metadata may lead to harmful corrections due to the high noise in sparse networks.

Section: Discussionmentioning

confidence: 99%

“…The measure of efficiency E is defined as the percentage of the RMSE of the homogenized dataset relative to the RMSE of the raw data (Domonkos et al ., ):

E = \frac{R M S E_{raw} - R M S E_{hom}}{R M S E_{raw}} \times 100 .

…”

Section: Methodsmentioning

confidence: 99%

The influence of station density on climate data homogenization

Gubler

Hunziker

Begert

et al. 2017

“…These spurious signals, called inhomogeneities, can greatly affect climate analysis and should be removed from the time series to the highest possible degree. To meet this criterion, a variety of homogenization methods have been developed in recent decades (Reeves et al , ; Costa and Soares, ; Domonkos et al , ).…”

Section: Introductionmentioning

confidence: 99%

Ensemble homogenization of Slovenian monthly air temperature series

Vertacnik

Dolinar

Bertalanič

et al. 2015

This paper presents an attempt to obtain high-quality data series of monthly air temperature for Slovenian stations network in the period from 1961 to 2011. Intensive quality control procedure was applied to mean, maximum and minimum air temperature datasets from the Slovenian Environment Agency. Recently developed semi-automatic homogenization tool HOMER (HOMogenisation softwarE in R) was used to homogenize the selected high-quality datasets. To estimate the reliability of homogenized datasets, three to six experts independently homogenized the same datasets or their subsets. Different homogenization parameter settings were used by each of the experts, thus comprising ensemble homogenization experiment. Resulting datasets were compared by break statistics, root-mean-squared-difference (RMSD) of monthly and annual values, and RMSD of the long-term trend. This semi-automatic homogenization approach based on metadata gave more reliable homogenization results than a fully automatic approach without metadata. While the network-wide linear trend of the dataset did not change after semi-automatic homogenization was applied, the distribution of the trends of individual stations became spatially more uniform. The arithmetic mean of the homogenized datasets of three experts was assigned as a reference homogenized dataset and it was compared with some publicly available homogenized datasets. The calculated linear trend on an annual level for Slovenia is strongly positive in all datasets, though the trend values are significantly different between the datasets. We conclude that the warming trend of near-surface air temperature in Slovenia in 1961-2011 is significant and unequivocal in all seasons, except for autumn. Mean, maximum and minimum temperature series indicate linear trend of around 0.3-0.4 ∘ C decade -1 on an annual level.

“…As observed temperature time series include five to seven breaks per 100 years on average (Menne et al , ; Venema et al , ), or even more due to hidden short‐term biases (Domonkos, ; Rienzner and Gandolfi, ) multiple break methods are of key importance in providing high level solutions for homogenisation tasks particularly in relation to the homogenisation of temperature. Efficiency tests prove that multiple break methods generally outperform other homogenisation methods (Domonkos, ,; Domonkos et al , ; Venema et al , ). Although some inhomogeneities result in gradually increasing biases instead of abrupt shifts of the means (e.g.…”

Section: Introductionmentioning

confidence: 99%

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

Domonkos¹,

Coll

2016

Self Cite

ABSTRACT:The development of Adapted Caussinus-Mestre Algorithm for homogenising Networks of Temperature series (ACMANT), one of the most successful homogenisation methods tested by the European project COST ES0601 (HOME) has been continued. The third generation of the software package 'ACMANT3' contains six programmes for homogenising temperature values or precipitation totals. These incorporate two models of the annual cycle of temperature biases and homogenisation either on a monthly or daily time scale. All ACMANT3 programmes are fully automatic and the method is therefore suitable for homogenising large datasets. This paper describes the theoretical background of ACMANT and the recent developments, which extend the capabilities, and hence, the application of the method. The most important novelties in ACMANT3 are: the ensemble pre-homogenisation with the exclusion of one potential reference composite in each ensemble member; the use of ordinary kriging for weighting reference composites; the assessment of seasonal cycle of temperature biases in case of irregular-shaped seasonal cycles. ACMANT3 also allows for homogenisation on the daily scale including for break timing assessment, gap filling and ANOVA application on the daily time scale. Examples of efficiency tests of monthly temperature homogenisation using artificially developed but realistic test datasets are presented. ACMANT3 can be characterized by improved efficiency in comparison with earlier ACMANT versions, high missing data tolerance and improved user friendliness. Discussion concerning when the use of an automatic homogenisation method is recommended is included, and some caveats in relation to how and when ACMANT3 should be applied are provided.