Search Results (4)

The aim of the homogenization of climatic time series is to remove non-climatic biases from the observed data, which are caused by technical or environmental changes during the period of observations. This bias removal is generally more successful for long-term trends and annual means than for monthly and daily values. The homogenization of probability distribution (HPD) may improve data accuracy even for daily data when the signal-to-noise ratio favors its application. HPD can be performed by quantile matching or spatial interpolations, but both of them have drawbacks. This study presents a new algorithm which helps to increase homogenization accuracy in all temporal and spatial scales. The new method is similar to quantile matching, but section mean values of the probability distribution function (PDF) are compared instead of individual daily values. The input dataset of the algorithm is identical with the homogenization results for section means of the studied time series. The algorithm decides about statistical significance for each break detected during the homogenization of the section means, and skips the insignificant breaks. Correction terms for removing the inhomogeneity biases of PDF are calculated jointly by a Benova-like equation system, a low pass filter is used for smoothing the prime results, and the mean value of the input time series between two consecutive detected breaks is preserved for each of such sections. This initial version does not deal with seasonal variations either during HPD or in other steps of the homogenization. The method has been tested connecting HPD to ACMANTv5.3, and using overall 8 wind speed and relative humidity datasets of the benchmark of European project INDECIS. The results show 4 to 12 percent RMSE reduction by HPD in all temporal scales, except for the extreme tails where a part of the results are weaker. Full article

Homogenization of climatic time series aims to remove non-climatic biases which come from the technical changes in climate observations. The method comparison tests of the Spanish MULTITEST project (2015–2017) showed that ACMANT was likely the most accurate homogenization method available at that time, although the tested ACMANTv4 version gave suboptimal results when the test data included synchronous breaks for several time series. The technique of combined time series comparison was introduced to ACMANTv5 to better treat this specific problem. Recently performed tests confirm that ACMANTv5 adequately treats synchronous inhomogeneities, but the accuracy has slightly worsened in some other cases. The results for a known daily temperature test dataset for four U.S. regions show that the residual errors after homogenization may be larger with ACMANTv5 than with ACMANTv4. Further tests were performed to learn more about the efficiencies of ACMANTv4 and ACMANTv5 and to find solutions for the problems occurring with the new version. Planned changes in ACMANTv5 are presented in the paper along with related test results. The overall results indicate that the combined time series comparison can be kept in ACMANT, but smaller networks should be generated in the automatic networking process of the method. To improve further the homogenization methods and to obtain more reliable and more solid knowledge about their accuracies, more synthetic test datasets mimicking the true spatio-temporal structures of real climatic data are needed. Full article

Long time series of observed climate data are often affected by changes in the technical conditions of the observations, which cause non-climatic biases, so-called inhomogeneities. Such inhomogeneities can be removed, at least partly, by the spatial comparison and statistical analysis of the data, and by the use of documented information about the historical changes in technical conditions, so-called metadata. Large datasets need the use of automatic or semiautomatic homogenization methods, but the effective use of non-quantitative metadata information within automatic procedures is not straightforward. The traditional approach suggests that a piece of metadata can be considered in statistical homogenizations only when the statistical analysis indicates a higher than threshold probability of inhomogeneity occurrence at or around the date of the metadata information. In this study, a new approach is presented, which suggests that the final inhomogeneity corrections should be done by the ANOVA correction model, and all the metadata dates likely indicating inhomogeneities according to the content of the metadata should be included in that correction step. A large synthetic temperature benchmark dataset has been created and used to test the performance of the ACMANT homogenization method both with traditional metadata use and with the suggested new method. The results show that while the traditional metadata use provides only 1–4% error reduction in comparison with the residual errors obtained by the homogenization without metadata, this ratio reaches 8–15% in the new, permissive use of metadata. The usefulness of metadata depends on the test dataset properties and homogenization method, these aspects are examined and discussed. Full article

The removal of non-climatic biases, so-called inhomogeneities, from long climatic records needs sophistically developed statistical methods. One principle is that the differences between a candidate series and its neighbor series are usually analyzed instead of the candidate series directly, in order to neutralize the possible impacts of regionally common natural climate variation on the detection of inhomogeneities. In most homogenization methods, two main kinds of time series comparisons are applied, i.e., composite reference series or pairwise comparisons. In composite reference series, the inhomogeneities of neighbor series are attenuated by averaging the individual series, and the accuracy of homogenization can be improved by the iterative improvement of composite reference series. By contrast, pairwise comparisons have the advantage that coincidental inhomogeneities affecting several station series in a similar way can be identified with higher certainty than with composite reference series. In addition, homogenization with pairwise comparisons tends to facilitate the most accurate regional trend estimations. A new time series comparison method is presented here, which combines the use of pairwise comparisons and composite reference series in a way that their advantages are unified. This time series comparison method is embedded into the Applied Caussinus-Mestre Algorithm for homogenizing Networks of climatic Time series (ACMANT) homogenization method, and tested in large, commonly available monthly temperature test datasets. Further favorable characteristics of ACMANT are also discussed. Full article