A novel approach for the detection of inhomogeneities affecting climate time series

Journal of Applied Meteorology and Climatology

Volumn 51, Issue 2, 2012, Pages 317-326

  Toreti, Andrea ^a   Kuglitsch, Franz G ^b   Xoplaki, Elena ^a,b   Luterbacher, Jürg ^a  

^a JUSTUS LIEBIG UNIVERSITY   (Germany)

^b UNIVERSITY OF BERN   (Switzerland)

Author keywords

Changepoint analysis; Statistics; Time series

Indexed keywords

CHANGE-POINT ANALYSIS; CHANGE-POINTS; CLIMATE ANALYSIS; CLIMATE VARIABLES; DATA SETS; FUTURE APPLICATIONS; GENERIC CONTEXTS; INFORMATICS; INHOMOGENEITIES; MILAN , ITALY; SUDDEN CHANGE; TIME-SERIES SEGMENTATION; WEATHER STATIONS; WINTER PRECIPITATION;

DATA PROCESSING; HIDDEN MARKOV MODELS; HOMOGENIZATION METHOD; METADATA; STATISTICS;

TIME SERIES;

CLIMATE VARIATION; DATA SET; HOMOGENEITY; INHOMOGENEITY; MARKOV CHAIN; NUMERICAL MODEL; SEGMENTATION; TIME SERIES ANALYSIS;

ITALY; LOMBARDY; MILAN; MILANO [LOMBARDY];

EID: 84857751472     PISSN: 15588424     EISSN: None     Source Type: Journal    
DOI: 10.1175/JAMC-D-10-05033.1     Document Type: Article

References (68)

1. Aguilar, E., I. Auer, M. Brunet, T. C. Peterson, and J. Wieringa, 2003: Guidance on metadata and homogenization. WMO TD 1186, 53 pp.
2. Akaike, H., 1973: Information theory and an extension of the maximum likelihood principle. Proceedings of the Second International Symposium on Information Theory, B. N. Petrov and F. Csádki, Eds., Akadémiai Kiadó, 267-281.
3. Alexandersson, H., and A. Moberg, 1997: Homogenization of Swedish temperature data. Part I: Homogeneity test for linear trends. Int. J. Climatol., 17, 25-34.
4. Bai, J., and P. Perron, 1998: Estimating and testing linear models with multiple structural changes. Econometrica, 66, 47-78.
5. Baker, J., 1987: Reducing bias and inefficiency in the selection algorithm. Proc. Second Int. Conf. on Genetic Algorithms and Their Application, Cambridge, MA, Massachusetts Institute of Technology, 14-21.
6. Blickle, T., and L. Thiele, 1995: A mathematical analysis of tournament selection. Proc. Sixth Int. Conf. on Genetic Algorithms, Pittsburgh, PA, University of Pittsburgh, 9-16.
7. Cappé, O., E. Moulines, and T. Rydén, 2005: Inference in Hidden Markov Models. Springer, 672 pp.
8. Caussinus, H., and O. Mestre, 2004: Detection and correction of artificial shifts in climate series. Appl. Stat., 53, 405-425.
9. Celeux, G., and J. B. Durand, 2008: Selecting hiddenMarkov model state number with cross-validated likelihood. Comput. Stat., 23, 541-564.
10. Chambaz, A., A. Garivier, and E. Gassiat, 2009: A minimum description length approach to hiddenMarkovmodelswith Poisson and Gaussian emissions. Application to order identification. J. Stat. Plann. Inference, 139, 962-977.
11. Davis, R. A., T. C. M. Lee, and G. A. Rodriguez-Yam, 2006: Structural break estimation for nonstationary time series models. J. Amer. Stat. Assoc., 101, 223-239.
12. DeGaetano, A. T., 2006: Attributes of several methods for detecting discontinuities in mean temperature series. J. Climate, 19, 838-853.
13. Easterling, D. R., and T. C. Peterson, 1995: A new method for detecting undocumented discontinuities in climatological time series. Int. J. Climatol., 15, 369-377.
14. Ephraim, Y., and W. J. J. Roberts, 2005: Revisiting autoregressive hidden Markov modeling of speech signals. IEEE Signal Process. Lett., 12, 166-169.
15. Forney, G. D., 1973: The Viterbi algorithm. Proc. IEEE, 61, 268-278.
16. Fridlyand, J., A. M. Snijders, D. Pinkel, D.G. Albertson, and A. N. Jain, 2004: Hidden Markov models approach to the analysis of array CGH data. J. Multivariate Anal., 90, 132-153.
17. Frühwirth-Schnatter, S., 2006: Finite Mixture and Markov Switching Models. Springer, 492 pp.
18. Gassiat, E., 2002: Likelihood ratio inequalities with applications to various mixtures. Ann. Inst. Henri Poincaré, 38, 897-906.
19. Grünwald, P. D., 2007: The Minimum Description Length Principle. MIT Press, 703 pp.
20. Holland, J. H., 1992: Adaptation in Natural and Artificial Systems. MIT Press, 211 pp.
21. Hubert, P., 1997: Change points in meteorological time analysis. Application of Time Series Analysis in Astronomy and Meteorology, T. Subba Rao, M. B. Priestly, and O. Lessi, Eds., Chapman and Hall, 399-412.
22. Jann, A., 2006: Genetic algorithms: Towards their use in the homogenization of climatological records. Croatian Meteor. J., 41, 3-19.
23. Jansen, E., and Coauthors, 2007: Paleoclimate. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 433-497.
24. Juang, B. H., and L. R. Rabiner, 1991: Hidden Markov models for speech recognition. Technometrics, 33, 251-272.
25. Kehagias, A., 2004: A hidden Markov model segmentation procedure for hydrological and environmental time series. Stochastic Environ. Res. Risk Assess., 18, 117-130.
26. Kehagias, A., and V. Fortin, 2006: Time series segmentation with shifting means hidden Markov models. Nonlinear Processes Geophys., 13, 1-14.
27. Kehagias, A., E. Nidelkou, and V. Petridis, 2005: A dynamic programming segmentation procedure for hydrological and environmental time series. Stochastic Environ. Res. Risk Assess., 20, 77-94.
28. Kim, H. J., and D. Siegmund, 1989: The likelihood ratio test for a change point in simple linear regression. Biometrika, 76, 409-423.
29. Koop, G., and S. M. Potter, 2009: Prior elicitation in multiple change-point models. Int. Econ. Rev., 50, 751-772.
30. Kuglitsch, F. G., A. Toreti, E. Xoplaki, P. M. Della-Marta, J. Luterbacher, and H. Wanner, 2009: Homogenization of daily maximum temperature series in the Mediterranean. J. Geophys. Res., 114, D15108, doi:10.1029/2008JD011606.
31. Kwong, S., C. W. Chau, K. F. Man, and K. S. Tang, 2001: Optimisation of HMM topology and its model parameters by genetic algorithms. Pattern Recognit., 34, 509-522.
32. Lee, T. C. M., 2001: An introduction to coding theory and the twopart minimum description length principle. Int. Stat. Rev., 69, 169-183.
33. Li, S., and R. Lund, 2012: Multiple changepoint detection via genetic algorithms. J. Climate, 25, 674-686.
34. Lu, Q., R. Lund, and T. C. M. Lee, 2010: An MDL approach to the climate segmentation problem. Ann. Appl. Stat., 4, 299-319.
35. Lund, R., X. L. Wang, Q. Lu, J. Reeves, C. Gallagher, and Y. Feng, 2007: Changepoint detection in periodic and autocorrelated time series. J. Climate, 20, 5178-5190.
36. MacDonald, I. L., and W. Zucchini, 1997: Hidden Markov and Other Models for Discrete-Valued Time Series. Chapman and Hall, 256 pp.
37. MacKay, R. J., 2002: Estimating the order of a hidden Markov model. Can. J. Stat., 30, 573-589.
38. Menne, M. J., and C. N.Williams, 2005:Detection of undocumented changepoints using multiple test statistics and composite reference series. J. Climate, 18, 4271-4286.
39. Mielke, P. W., K. J. Berry, and G. W. Brier, 1981: Application of multi-response permutation procedures for examining seasonal changes in monthly mean sea level pressure patterns. Mon. Wea. Rev., 109, 120-126.
40. Miranda, P. M. A., and A. R. Tomé, 2009: Spatial structure of the evolution of surface temperature (1951-2004).ClimaticChange, 93, 269-284.
41. Moberg, A., and Coauthors, 2006: Indices for daily temperature and precipitation extremes in Europe analyzed for the period 1901-2000. J.Geophys. Res., 111, D22106, doi:10.1029/2006JD007103.
42. Pawson, S., K. Labitzke, and S. Leder, 1998: Stepwise changes in stratospheric temperature. Geophys. Res. Lett., 25, 2157-2160.
43. Perreault, L., J. Bernier, B. Bobee, and E. Parent, 2000: Bayesian change-point analysis in hydrometeorological time series. Part 1. The normal model revisited. J. Hydrol., 235, 221-241.
44. Peterson, T. C., and D. R. Easterling, 1994: Creation of homogeneous composite climatological reference series. Int. J. Climatol., 14, 671-679.
45. Rabiner, L. R., 1989: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE, 77, 257-286.
46. Randall, D. A., and Coauthors, 2007: Climate models and their evaluation. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 589-662.
47. Ray, B. K., and R. S. Tsay, 2002: Bayesian method for change-point detection in long-range dependent processes. J. Time Ser. Anal., 23, 687-705.
48. Reeves, J., J. Chen, X. L. Wang, R. Lund, and Q. Lu, 2007: A review and comparison of changepoint detection techniques for climate data. J. Appl. Meteor. Climatol., 46, 900-915.
49. Robinson, L. F., T. D. Wager, and M. A. Lindquist, 2010: Change point estimation in multi-subject fMRI studies. NeuroImage, 49, 1581-1592.
50. Schwarz, G., 1978: Estimating the dimension of a model. Ann. Stat., 6, 461-464.
51. Swami, D. K., and R. C. Jain, 2006: PAMC: Partitioning around medoids for classification. Inf. Technol. J., 5, 1102-1105.
52. Tai, Y. C., M. N. Kvale, and J. S. Witte, 2010: Segmentation and estimation for SNP microarrays: A Bayesian multiple changepoint approach. Biometrics, 66, 675-683.
53. Tartakovsky, A. G., B. L. Rozovskii, R. Blažek, and H. Kim, 2006: Detection of intrusions in information systems by sequential change-point methods. Stat. Methodol., 3, 252-293.
54. Toreti, A., and F. Desiato, 2008: Temperature trend over Italy from 1961 to 2004. Theor. Appl. Climatol., 91, 51-58.
55. Toreti, A., and F. Desiato, G. Fioravanti, W. Perconti, and F. Desiato, 2009: Annual and seasonal precipitation over Italy from 1961 to 2006. Int. J. Climatol., 29, 1976-1987.
56. Toreti, A., and F. Desiato, G. Fioravanti, W. Perconti, and F. Desiato, F. G. Kuglitsch, E. Xoplaki, J. Luterbacher, and H. Wanner, 2010a: A novel method for the homogenization of daily temperature series and its relevance for climate change analysis. J. Climate, 23, 5325-5331.
57. Toreti, A., and F. Desiato, G. Fioravanti, W. Perconti, and F. Desiato, E. Xoplaki, D. Maraun, F. G. Kuglitsch, H. Wanner, and J. Luterbacher, 2010b: Characterisation of extreme winter precipitation in Mediterranean coastal sites and associated anomalous atmospheric circulation patterns. Nat. Hazards Earth Syst., 10, 1037-1050.
58. Trenberth, K. E., 1990: Recent observed interdecadal climate changes in the Northern Hemisphere. Bull. Amer. Meteor. Soc., 71, 988-993.
59. Viterbi, A. J., 2006: A personal history of the Viterbi algorithm. IEEE Signal Process. Mag., 120, 120-122.
60. Wang, X. L., 2008a: Accounting for autocorrelation in detecting mean shifts in climate data series using the penalized maximal t or F test. J. Appl. Meteor. Climatol., 47, 2423-2444.
61. Wang, X. L., 2008b: Penalized maximal F test for detecting undocumented mean shift without trend change. J. Atmos. Oceanic Technol., 25, 368-384.
62. Wang, X. L., Q. H. Wen, and Y. Wu, 2007: Penalized maximal t test for detecting undocumented mean change in climate data series. J. Appl. Meteor. Climatol., 46, 916-931.
63. Welch, L. R., 2003: Hidden Markov models and the Baum-Welch algorithm. IEEE Inf. Theory Soc. Newsl., 53, 10-13.
64. Whitley, D., 1994: A genetic algorithm tutorial. Stat. Comput., 4, 65-85.
65. Wilks, D. S., 1999: Simultaneous stochastic simulation of daily precipitation, temperature and solar radiation at multiple sites in complex terrain. Agric. For. Meteor., 96, 85-101.
66. Wilks, D. S., 2005: Statistical Methods in the Atmospheric Sciences. Academic Press, 648 pp.
67. Won, K. J., A. Prügel-Bennett, and A. Krogh, 2004: TrainingHMM structure with genetic algorithm for biological sequence analysis. Bioinformatics, 20, 3613-3619.
68. Xie, Y., J. Yu, and B. Ranneby, 2008: A general autoregressive model with Markov switching: Estimation and consistency. Math. Methods Stat., 17, 228-240.