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DOI	10.1175/JCLI-D-20-0611.1
	Efficiency of time series homogenization: Method comparison with 12 monthly temperature test datasets
	Domonkos P.; Guijarro J.A.; Venema V.; Brunet M.; Sigró J.
发表日期	2021
ISSN	08948755
起始页码	2877
结束页码	2891
卷号	34 期号:8
英文摘要	The aim of time series homogenization is to remove nonclimatic effects, such as changes in station location, instrumentation, observation practices, and so on, from observed data. Statistical homogenization usually reduces the nonclimatic effects but does not remove them completely. In the Spanish ''MULTITEST'' project, the efficiencies of automatic homogenization methods were tested on large benchmark datasets of a wide range of statistical properties. In this study, test results for nine versions, based on five homogenization methods-the adapted Caussinus-Mestre algorithm for the homogenization of networks of climatic time series (ACMANT), ''Climatol,'' multiple analysis of series for homogenization (MASH), the pairwise homogenization algorithm (PHA), and ''RHtests''-are presented and evaluated. The tests were executed with 12 synthetic/surrogate monthly temperature test datasets containing 100-500 networks with 5-40 time series in each. Residual centered root-mean-square errors and residual trend biases were calculated both for individual station series and for network mean series. The results show that a larger fraction of the nonclimatic biases can be removed from station series than from network-mean series. The largest error reduction is found for the long-term linear trends of individual time series in datasets with a high signal-to-noise ratio (SNR), where the mean residual error is only 14%-36% of the raw data error. When the SNR is low, most of the results still indicate error reductions, although with smaller ratios than for large SNR. In general, ACMANT gave the most accurate homogenization results. In the accuracy of individual time series ACMANT is closely followed by Climatol, and for the accurate calculation of mean climatic trends over large geographical regions both PHA and ACMANT are recommended. © 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
英文关键词	Algorithms; Climate records; Data quality control; Temperature; Time series
语种	英语
scopus关键词	Efficiency; Errors; Geographical regions; Homogenization method; Large dataset; Mean square error; Signal to noise ratio; Time series; Accurate calculations; Benchmark datasets; High signalto-noise ratios (SNR); Method comparison; Root mean square errors; Station location; Statistical properties; Temperature test; Time series analysis; air temperature; algorithm; comparative study; data set; quality control; signal-to-noise ratio; time series
来源期刊	Journal of Climate
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178659
作者单位	Tortosa, Spain; State Meteorological Agency (AEMET), Unit of Islas Baleares, Palma, Spain; Meteorological Institute, University of Bonn, Bonn, Germany; Centre for Climate Change, Universitat Rovira i Virgili, Vila-seca, Spain; Climatic Research Unit, University of East Anglia, Norwich, United Kingdom
推荐引用方式 GB/T 7714	Domonkos P.,Guijarro J.A.,Venema V.,et al. Efficiency of time series homogenization: Method comparison with 12 monthly temperature test datasets[J],2021,34(8).
APA	Domonkos P.,Guijarro J.A.,Venema V.,Brunet M.,&Sigró J..(2021).Efficiency of time series homogenization: Method comparison with 12 monthly temperature test datasets.Journal of Climate,34(8).
MLA	Domonkos P.,et al."Efficiency of time series homogenization: Method comparison with 12 monthly temperature test datasets".Journal of Climate 34.8(2021).