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Newest Developments of Acmant : Volume 6, Issue 1 (31/01/2011)

By Domonkos, P.

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Book Id: WPLBN0003990334
Format Type: PDF Article :
File Size: Pages 5
Reproduction Date: 2015

Title: Newest Developments of Acmant : Volume 6, Issue 1 (31/01/2011)  
Author: Domonkos, P.
Volume: Vol. 6, Issue 1
Language: English
Subject: Science, Advances, Science
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2011
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Poza, R., Efthymiadis, D., & Domonkos, P. (2011). Newest Developments of Acmant : Volume 6, Issue 1 (31/01/2011). Retrieved from http://www.ebooklibrary.org/


Description
Description: Centre for Climate Change (C3), Geography Dept., University Rovira i Virgili, Campus Terres de l'Ebre, C/Betánia 5 Tortosa, 43500 Spain. The seasonal cycle of radiation intensity often causes a marked seasonal cycle in the inhomogeneities (IHs) of observed temperature time series, since a substantial portion of them have direct or indirect connection to radiation changes in the micro-environment of the thermometer. Therefore the magnitudes of temperature IHs tend to be larger in summer than in winter. A new homogenisation method, the Adapted Caussinus – Mestre Algorithm for Networks of Temperature series (ACMANT) has recently been developed which treats appropriately the seasonal changes of IH-sizes in temperature time series. The performance of ACMANT was proved to be among the best methods (together with PRODIGE and MASH) in the efficiency test procedure of COST ES0601 project. A further improved version of the ACMANT is described in this paper. In the new version the ANOVA procedure is applied for correcting inhomogeneities, and with this change the iterations applied in the earlier version have become unnecessary. Some other modifications have also been made, from which the most important one is the new way for estimating the timings of IHs. With these modifications the efficiency of the ACMANT has become even higher, therefore its use is strongly recommended when networks of monthly temperature series from mid- or high geographical latitudes are subjected to homogenisation. The paper presents the main properties and the operation of the new ACMANT.

Summary
Newest developments of ACMANT

Excerpt
Caussinus, H. and Lyazrhi, F.: Choosing a linear model with a random number of change-points and outliers, Ann. Inst. Statist. Math., 49/4, 761–775, doi:10.1023/A:1003230713770, 1997.; Caussinus, H. and Mestre, O.: Detection and correction of artificial shifts in climate series, J. Roy. Stat. Soc., Series C53, 405–425, doi:10.1111/j.1467-9876.2004.05155.x, 2004.; Domonkos, P.: Testing of homogenisation methods: purposes, tools and problems of implementation, Proceedings of the 5th Seminar and Quality Control in Climatological Databases, CD, 126–145, 2006.; Hawkins, D. M.: On the choice of segments in piecewise approximation, J. Inst. Math. Appl., 9, 250–256, doi:10.1093/imamat/9.2.250, 1972.; Menne, M. J. and Williams Jr., C. N.: Homogenization of temperature series via pairwise comparisons, J. Climate, 22, 1700–1717, doi:10.1175/2008JCLI2263.1, 2009.; Aguilar, E., Auer, I., Brunet, M., Peterson, T. C., and Wieringa, J.: WMO Guidelines on climate metadata and homogenization, WCDMP-No. 53, WMO-TD No 1186, WMO, Geneva, 2003.; Alexandersson, H.: A homogeneity test applied to precipitation data, J. Climatol., 6, 661–675, doi:10.1002/joc.3370060607, 1986.; Auer, I. and 24 coauthors: A new instrumental precipitation dataset for the greater Alpine region for the period 1800–2002, Int. J. Climatol., 25, 139–166, doi:10.1002/joc.1135, 2005.; Brunet, M., Asin, J., Sigró, J., Bañon, M., García, F., Aguilar, E., Palenzuela, J. E., Peterson, T. C., and Jones, P.: The minimization of the screen bias from ancient Western Mediterranean air temperature records: an exploratory statistical analysis. Int. J. Climatol., doi:10.1002/joc.2192, 2011.; Domonkos, P. and Štěpánek, P.: Statistical characteristics of detectable inhomogeneities in observed meteorological time series, Studia Geoph. et Geod., 53, 239–260, doi:10.007/ s11200-009-0015-9, 2009.; Gérard-Marchant, P. G. F., Stooksbury, D. E., and Seymour, L.: Methods for starting the detection of undocumented multiple changepoints, J. Climate, 21, 4887–4899, doi:10.1175/2008JCLI1956.1, 2008.; Peterson, T. C. and Easterling, D. R.: Creation of homogeneous composite climatological reference series, Int. J. Climatol., 14, 671–679, doi:10.1002/joc.3370140606, 1994.; Sherwood, S. C.: Simultaneous detection of climate change and observing biases in a network with incomplete sampling, J. Climate, 20, 4047–4062, doi:10.1175/JCLI4215.1, 2007.; Titchner, H. A., Thorne, P. W., McCarthy, M. P., Tett, S. F. B., Haimberger, L., and Parker, D. E.: Critically reassessing tropospheric temperature trends from radiosondes using realistic validation experiments, J. Climate, 22, 465–485, doi:10.1175/2008JCLI2419.1, 2009.; Venema, V., Mestre, O., and the COST HOME Team: Benchmark database, EGU General Assembly, Vienna, Austria, 3–7 May 2010, EGU CL4.6-13357, 2010.

 

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