Adapted Caussinus-Mestre Algorithm for Networks of Temperature series (ACMANT)

Author(s)
Peter Domonkos

ABSTRACT

Any change in technical or environmental conditions of observations may result in bias from the precise values of observed climatic variables. The common name of these biases is inhomogeneity (IH). IHs usually appear in a form of sudden shift or gradual trends in the time series of any variable, and the timing of the shift indicates the date of change in the conditions of observation. The seasonal cycle of radiation intensity often causes marked seasonal cycle in the IHs of observed temperature time series, since a substantial portion of them has 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 (ACMANT) has recently been developed which treats in a special way the seasonal changes of IH-sizes in temperature time series. The ACMANT is a further development of the Caussinus-Mestre method, that is one of the most effective tool among the known homogenising methods. The ACMANT applies a bivariate test for searching the timings of IHs, the two variables are the annual mean temperature and the amplitude of seasonal temperature-cycle. The ACMANT contains several further innovations whose efficiencies are tested with the benchmark of the COST ES0601 project. The paper describes the properties and the operation of ACMANT and presents some verification results. The results show that the ACMANT has outstandingly high performance. The ACMANT is a recommended method for homogenising networks of monthly temperature time series that observed in mid- or high geographical latitudes, because the harmonic seasonal cycle of IH-size is valid for these time series only.

Any change in technical or environmental conditions of observations may result in bias from the precise values of observed climatic variables. The common name of these biases is inhomogeneity (IH). IHs usually appear in a form of sudden shift or gradual trends in the time series of any variable, and the timing of the shift indicates the date of change in the conditions of observation. The seasonal cycle of radiation intensity often causes marked seasonal cycle in the IHs of observed temperature time series, since a substantial portion of them has 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 (ACMANT) has recently been developed which treats in a special way the seasonal changes of IH-sizes in temperature time series. The ACMANT is a further development of the Caussinus-Mestre method, that is one of the most effective tool among the known homogenising methods. The ACMANT applies a bivariate test for searching the timings of IHs, the two variables are the annual mean temperature and the amplitude of seasonal temperature-cycle. The ACMANT contains several further innovations whose efficiencies are tested with the benchmark of the COST ES0601 project. The paper describes the properties and the operation of ACMANT and presents some verification results. The results show that the ACMANT has outstandingly high performance. The ACMANT is a recommended method for homogenising networks of monthly temperature time series that observed in mid- or high geographical latitudes, because the harmonic seasonal cycle of IH-size is valid for these time series only.

KEYWORDS

Statistical Method Development, Observed Climatic Data, Temperature, Time Series Analysis, Data Quality Control, Homogenization, Efficiency

Statistical Method Development, Observed Climatic Data, Temperature, Time Series Analysis, Data Quality Control, Homogenization, Efficiency

Cite this paper

nullP. Domonkos, "Adapted Caussinus-Mestre Algorithm for Networks of Temperature series (ACMANT),"*International Journal of Geosciences*, Vol. 2 No. 3, 2011, pp. 293-309. doi: 10.4236/ijg.2011.23032.

nullP. Domonkos, "Adapted Caussinus-Mestre Algorithm for Networks of Temperature series (ACMANT),"

References

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[2] H. B. Mann and D. R. Whitney, “On a Test of Whether One of Two Random Variables is Stochastically Larger than the other,” The Annals of Mathematical Statistics, Vol. 18, No. 1, 1947, pp. 50-60. doi:10.1214/aoms/1177730491

[3] J. R. Lanzante, “Resistant, Robust and Non-Parametric Techniques for the Analysis of Climate Data: Theory and Examples, Including Applications to Historical Radiosonde Stationdata,” International Journal of Climatology, Vol. 16, No. 11, 1996, pp. 1197-1226. doi:10.1002/(SICI)1097-0088(199611)16:11<1197::AID-JOC89>3.0.CO;2-L

[4] A. R. Solow, “Testing for Climate Change: An Application of the Two-Phase Regression Model,” Journal of Climate and Applied Meteorology, Vol. 26, No. 10, 1987, pp. 1401-1405. doi:10.1175/1520-0450(1987)026<1401:TFCCAA>2.0.CO;2

[5] L. A. Vincent, “A Technique for the Identification of Inhomogeneities in Canadian Temperature Series,” Journal of Climate, Vol. 11, No. 5, 1998, pp. 1094-1104. doi:10.1175/1520-0442(1998)011<1094:ATFTIO>2.0.CO;2

[6] H. Alexandersson, “A Homogeneity Test Applied to Precipitation Data,” Journal of Climatology, Vol. 6, No. 6, 1986, pp. 661-675. doi:10.1002/joc.3370060607

[7] X. L. Wang, Q. H. Wen and Y. Wu, “Penalized Maximal t Test for Detecting Undocumented Mean Change in Climate Data Series,” Journal of Applied Meteorology and Climatology, Vol. 46, No. 6, 2007, pp. 916-931. doi:10.1175/JAM2504.1

[8] T. Szentimrey, “Multiple Analysis of Series for Homogenization (MASH),” Second Seminar for Homogenization of Surface Climatological Data, WMO, Geneva, 1999, pp. 27-46.

[9] H. Caussinus and O. Mestre, “Detection and Correction of Artificial Shifts in Climate Series,” Journal of Royal Statistics Society Series C, Vol. 53, No. 3, 2004, pp. 405-425. doi:10.1111/j.1467-9876.2004.05155.x

[10] T. C. Peterson and 20 co-authors, “Homogeneity Adjustments of in Situ Atmospheric Climate Data: A Review,” International Journal of Climatology, Vol. 18, No. 13, 1998, pp. 1493-1517. doi:10.1002/(SICI)1097-0088(19981115)18:13<1493::AID-JOC329>3.0.CO;2-T

[11] E. Aguilar, I. Auer, M. Brunet, T. C. Peterson and J. Wieringa, “WMO Guidelines on Climate Metadata and Homogenization,” WMO, Geneva, 2003.

[12] J.-F. Ducré-Robitaille, L. A. Vincent and G. Boulet, “Comparison of Techniques for Detection of Discontinuities in Temperature Series,” International Journal of Climatology, Vol. 23, No. 9, 2003, pp. 1087-1101. doi:10.1002/joc.924

[13] I. Auer, et al., “A New Instrumental Precipitation Dataset for the Greater Alpine Region for the Period 1800-2002,” International Journal of Climatology, Vol. 25, No. 2, 2005, pp. 139-166. doi:10.1002/joc.1135

[14] M. J. Menne and C. N. Williams Jr., “Detection of Undocumented Changepoints Using Multiple Test Statistics and Composite Reference Series,” Journal of Climate, Vol. 18, No. 20, 2005, pp. 4271-4286. doi:10.1175/JCLI3524.1

[15] M. J. Menne and C. N. Williams Jr., “Homogenization of Temperature Series via Pairwise Comparisons,” Journal of Climate, Vol. 22, 2009, pp. 1700-1717.

[16] M. Brunet, M, O. Saladié, P. Jones, J. Sigró, E. Aguilar, A. Moberg, D. Lister, A. Walther, D. Lopez and C. Almarza, “The Development of a New Dataset of Spanish Daily Adjusted Temperature Series (SDATS) (1850-2003),” International Journal of Climatology, Vol. 26, No. 13, 2006, pp. 1777-1802. doi:10.1002/joc.1338

[17] M. Brunet, O. Saladié, P. Jones, J. Sigró, E. Aguilar, A. Moberg, D. Lister, A. Walther and C. Almarza, “A Case-Study/Guidance on the Development of Long-Term Daily Adjusted Temperature Datasets,” WC-DMP-66/ WMO-TD-1425, 2008.

[18] P. Domonkos, “Application of Objective Homogenization Methods: Inhomogeneities in Time Series of Temperature and Precipitation,” Id?járás, Vol. 110, 2006, pp. 63-87.

[19] M. Brunet, J. Asin, J. Sigró, M. Ba?on, F. García, E. Aguilar, J. E. Palenzuela, T. C. Peterson and P. Jones, “The Minimization of the Screen Bias from Ancient Western Mediterranean Air Temperature Records: An Exploratory Statistical Analysis,” International Journal of Climatology, Early View 2011,

[20] T. C. Peterson and D. R. Easterling, “Creation of Homogeneous Composite Climatological Reference Series,” International Journal of Climatology, Vol. 14, No. 6, 1994, pp. 671-679. doi:10.1002/joc.3370140606

[21] H. Alexandersson, and A. Moberg, “Homogenization of Swedish Temperature Data. Part I: Homogeneity Test for Linear Trends,” International Journal of Climatology, Vol. 17, No. 1, 1997, pp. 25-34. doi:10.1002/(SICI)1097-0088(199701)17:1<25::AID-JOC103>3.0.CO;2-J

[22] M. Begert, T. Schlegel and W. Krichhofer, “Homogeneous Temperature and Precipitation Series of Switzerland from 1864 to 2000,” International Journal of Climatology, Vol. 25, No. 1, 2005, pp. 65-80. doi:10.1002/joc.1118

[23] A. T. DeGaetano, “Attributes of Several Methods for Detecting Discontinuities in Mean Temperature Series,” Journal of Climate, Vol. 19, No. 5, 2006, pp. 838-853. doi:10.1175/JCLI3662.1

[24] D. M. Hawkins, “On the Choice of Segments in Piecewise Approximation,” IMA Journal of Applied Mathematics, Vol. 9, No. 2, 1972, pp. 250-256. doi:10.1093/imamat/9.2.250

[25] H. Caussinus and F. Lyazrhi, “Choosing a Linear Model with a Random Number of Change-Points and Outliers,” Ann. Inst. Statist. Math, Vol. 49, No. 4, 1997, pp. 761-775. doi:10.1023/A:1003230713770

[26] COST HOME home-page. http://www.meteo.uni-bonn.de/venema/themes/homogenisation/

[27] V. Venema, O. Mestre and the COST HOME Team, “Benchmark Database,” EGU General Assembly, Vienna, Austria, 3-7 May, 2010.

[28] P. Domonkos, “Testing of Homogenisation Methods: Purposes, Tools and Problems of Implementation,” Proceedings of the 5th Seminar and Quality Control in Climatological, Hungarian, 25-27 October 2006.

[29] Databases, (Ed. M. Lakatos, T. Szentimrey, Z. Bihari and S. Szalai), WCDMP-No. 71, 2008, pp. 126-145.

[30] P. Domonkos, “Efficiency Evaluation for Detecting Inhomogeneities by Objective Homogenisation Methods,” Theoretical and Applied Climatology, Early View 2011. doi:10.1007/s00704-011-0399-7

[1] T. A. Buishand, “Some Methods for Testing the Homogeneity of Rainfall Records,” Journal of Hydrology, Vol. 58, No. 1-2, 1982, pp. 11-27. doi:10.1016/0022-1694(82)90066-X

[2] H. B. Mann and D. R. Whitney, “On a Test of Whether One of Two Random Variables is Stochastically Larger than the other,” The Annals of Mathematical Statistics, Vol. 18, No. 1, 1947, pp. 50-60. doi:10.1214/aoms/1177730491

[3] J. R. Lanzante, “Resistant, Robust and Non-Parametric Techniques for the Analysis of Climate Data: Theory and Examples, Including Applications to Historical Radiosonde Stationdata,” International Journal of Climatology, Vol. 16, No. 11, 1996, pp. 1197-1226. doi:10.1002/(SICI)1097-0088(199611)16:11<1197::AID-JOC89>3.0.CO;2-L

[4] A. R. Solow, “Testing for Climate Change: An Application of the Two-Phase Regression Model,” Journal of Climate and Applied Meteorology, Vol. 26, No. 10, 1987, pp. 1401-1405. doi:10.1175/1520-0450(1987)026<1401:TFCCAA>2.0.CO;2

[5] L. A. Vincent, “A Technique for the Identification of Inhomogeneities in Canadian Temperature Series,” Journal of Climate, Vol. 11, No. 5, 1998, pp. 1094-1104. doi:10.1175/1520-0442(1998)011<1094:ATFTIO>2.0.CO;2

[6] H. Alexandersson, “A Homogeneity Test Applied to Precipitation Data,” Journal of Climatology, Vol. 6, No. 6, 1986, pp. 661-675. doi:10.1002/joc.3370060607

[7] X. L. Wang, Q. H. Wen and Y. Wu, “Penalized Maximal t Test for Detecting Undocumented Mean Change in Climate Data Series,” Journal of Applied Meteorology and Climatology, Vol. 46, No. 6, 2007, pp. 916-931. doi:10.1175/JAM2504.1

[8] T. Szentimrey, “Multiple Analysis of Series for Homogenization (MASH),” Second Seminar for Homogenization of Surface Climatological Data, WMO, Geneva, 1999, pp. 27-46.

[9] H. Caussinus and O. Mestre, “Detection and Correction of Artificial Shifts in Climate Series,” Journal of Royal Statistics Society Series C, Vol. 53, No. 3, 2004, pp. 405-425. doi:10.1111/j.1467-9876.2004.05155.x

[10] T. C. Peterson and 20 co-authors, “Homogeneity Adjustments of in Situ Atmospheric Climate Data: A Review,” International Journal of Climatology, Vol. 18, No. 13, 1998, pp. 1493-1517. doi:10.1002/(SICI)1097-0088(19981115)18:13<1493::AID-JOC329>3.0.CO;2-T

[11] E. Aguilar, I. Auer, M. Brunet, T. C. Peterson and J. Wieringa, “WMO Guidelines on Climate Metadata and Homogenization,” WMO, Geneva, 2003.

[12] J.-F. Ducré-Robitaille, L. A. Vincent and G. Boulet, “Comparison of Techniques for Detection of Discontinuities in Temperature Series,” International Journal of Climatology, Vol. 23, No. 9, 2003, pp. 1087-1101. doi:10.1002/joc.924

[13] I. Auer, et al., “A New Instrumental Precipitation Dataset for the Greater Alpine Region for the Period 1800-2002,” International Journal of Climatology, Vol. 25, No. 2, 2005, pp. 139-166. doi:10.1002/joc.1135

[14] M. J. Menne and C. N. Williams Jr., “Detection of Undocumented Changepoints Using Multiple Test Statistics and Composite Reference Series,” Journal of Climate, Vol. 18, No. 20, 2005, pp. 4271-4286. doi:10.1175/JCLI3524.1

[15] M. J. Menne and C. N. Williams Jr., “Homogenization of Temperature Series via Pairwise Comparisons,” Journal of Climate, Vol. 22, 2009, pp. 1700-1717.

[16] M. Brunet, M, O. Saladié, P. Jones, J. Sigró, E. Aguilar, A. Moberg, D. Lister, A. Walther, D. Lopez and C. Almarza, “The Development of a New Dataset of Spanish Daily Adjusted Temperature Series (SDATS) (1850-2003),” International Journal of Climatology, Vol. 26, No. 13, 2006, pp. 1777-1802. doi:10.1002/joc.1338

[17] M. Brunet, O. Saladié, P. Jones, J. Sigró, E. Aguilar, A. Moberg, D. Lister, A. Walther and C. Almarza, “A Case-Study/Guidance on the Development of Long-Term Daily Adjusted Temperature Datasets,” WC-DMP-66/ WMO-TD-1425, 2008.

[18] P. Domonkos, “Application of Objective Homogenization Methods: Inhomogeneities in Time Series of Temperature and Precipitation,” Id?járás, Vol. 110, 2006, pp. 63-87.

[19] M. Brunet, J. Asin, J. Sigró, M. Ba?on, F. García, E. Aguilar, J. E. Palenzuela, T. C. Peterson and P. Jones, “The Minimization of the Screen Bias from Ancient Western Mediterranean Air Temperature Records: An Exploratory Statistical Analysis,” International Journal of Climatology, Early View 2011,

[20] T. C. Peterson and D. R. Easterling, “Creation of Homogeneous Composite Climatological Reference Series,” International Journal of Climatology, Vol. 14, No. 6, 1994, pp. 671-679. doi:10.1002/joc.3370140606

[21] H. Alexandersson, and A. Moberg, “Homogenization of Swedish Temperature Data. Part I: Homogeneity Test for Linear Trends,” International Journal of Climatology, Vol. 17, No. 1, 1997, pp. 25-34. doi:10.1002/(SICI)1097-0088(199701)17:1<25::AID-JOC103>3.0.CO;2-J

[22] M. Begert, T. Schlegel and W. Krichhofer, “Homogeneous Temperature and Precipitation Series of Switzerland from 1864 to 2000,” International Journal of Climatology, Vol. 25, No. 1, 2005, pp. 65-80. doi:10.1002/joc.1118

[23] A. T. DeGaetano, “Attributes of Several Methods for Detecting Discontinuities in Mean Temperature Series,” Journal of Climate, Vol. 19, No. 5, 2006, pp. 838-853. doi:10.1175/JCLI3662.1

[24] D. M. Hawkins, “On the Choice of Segments in Piecewise Approximation,” IMA Journal of Applied Mathematics, Vol. 9, No. 2, 1972, pp. 250-256. doi:10.1093/imamat/9.2.250

[25] H. Caussinus and F. Lyazrhi, “Choosing a Linear Model with a Random Number of Change-Points and Outliers,” Ann. Inst. Statist. Math, Vol. 49, No. 4, 1997, pp. 761-775. doi:10.1023/A:1003230713770

[26] COST HOME home-page. http://www.meteo.uni-bonn.de/venema/themes/homogenisation/

[27] V. Venema, O. Mestre and the COST HOME Team, “Benchmark Database,” EGU General Assembly, Vienna, Austria, 3-7 May, 2010.

[28] P. Domonkos, “Testing of Homogenisation Methods: Purposes, Tools and Problems of Implementation,” Proceedings of the 5th Seminar and Quality Control in Climatological, Hungarian, 25-27 October 2006.

[29] Databases, (Ed. M. Lakatos, T. Szentimrey, Z. Bihari and S. Szalai), WCDMP-No. 71, 2008, pp. 126-145.

[30] P. Domonkos, “Efficiency Evaluation for Detecting Inhomogeneities by Objective Homogenisation Methods,” Theoretical and Applied Climatology, Early View 2011. doi:10.1007/s00704-011-0399-7