Domonkos, P. Time Series Homogenization with ACMANT: Comparative Testing of Two Recent Versions in Large-Size Synthetic Temperature Datasets. Climate2023, 11, 224.
Domonkos, P. Time Series Homogenization with ACMANT: Comparative Testing of Two Recent Versions in Large-Size Synthetic Temperature Datasets. Climate 2023, 11, 224.
Domonkos, P. Time Series Homogenization with ACMANT: Comparative Testing of Two Recent Versions in Large-Size Synthetic Temperature Datasets. Climate2023, 11, 224.
Domonkos, P. Time Series Homogenization with ACMANT: Comparative Testing of Two Recent Versions in Large-Size Synthetic Temperature Datasets. Climate 2023, 11, 224.
Abstract
Homogenization of climatic time series aims to remove non-climatic biases which come from the technical changes of 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, in spite of 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 has been introduced to ACMANTv5 in order to treat better this specific problem. Tests confirm that ACMANTv5 treats adequately synchronous inhomogeneities, but the accuracy has slightly worsened in some other cases. Results for a known daily temperature test dataset for 4 U.S. regions show that the residual errors after homogenization may be larger with ACMANTv5 than with ACMANTv4. Further tests have been performed to learn more about the efficiencies of ACMANTv4 and ACMANTv5, and to find solutions for the appearing problems with the new version. Planned changes in ACMANTv5 are presented in the paper along with connecting test results. The overall results indicate that the combined time series comparison can be kept in ACMANT, but smaller networks should be generated by the automatic networking process of the method. For the further improvements of homogenization methods and for obtaining more reliable and more solid knowledge about their accuracies, more synthetic test datasets mimicking fairly the true spatio-temporal structures of real climatic data would be in need.
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
