Advertisement

Mathematical Geosciences

, Volume 41, Issue 3, pp 291–305 | Cite as

Homogenization of Climate Data: Review and New Perspectives Using Geostatistics

  • Ana Cristina Costa
  • Amílcar Soares
Special Issue

Abstract

The homogenization of climate data is of major importance because non-climatic factors make data unrepresentative of the actual climate variation, and thus the conclusions of climatic and hydrological studies are potentially biased. A great deal of effort has been made in the last two decades to develop procedures to identify and remove non-climatic inhomogeneities. This paper reviews the characteristics of several widely used procedures and discusses the potential advantages of geostatistical techniques. In a case study, the geostatistical simulation approach is applied to precipitation data from 66 monitoring stations located in the southern region of Portugal (1980–2001). The results from this procedure are then compared with those from three well established statistical tests: the Standard normal homogeneity test (SNHT) for a single break, the Buishand range test, and the Pettit test. Promising results from the case study open new research perspectives on the homogenization of climate time series.

Keywords

Data quality Composite reference series Homogeneity tests Nonparametric tests 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aguilar E, Auer I, Brunet M, Peterson TC, Wieringa J (2003) Guidelines on climate metadata and homogenization. World Meteorological Organization, WMO-TD No. 1186, WCDMP No. 53, Geneva, Switzerland, 55 Google Scholar
  2. Alexandersson H (1986) A homogeneity test applied to precipitation data. J Climatol 6:661–675 CrossRefGoogle Scholar
  3. Alexandersson H, Moberg A (1997) Homogenization of Swedish temperature data. Part I: Homogeneity test for linear trends. Int J Climatol 17(1):25–34 CrossRefGoogle Scholar
  4. Allen RG, Pereira LS, Raes D, Smith M (1998) Statistical analysis of weather data sets. In: Food and Agriculture Organization of the United Nations (ed) Crop evapotranspiration – Guidelines for computing crop water requirements. FAO Irrigation and drainage paper 56, Rome, Annex IV Google Scholar
  5. Auer I, Böhm R, Jurković A, Orlik A, Potzmann R, Schöner W, Ungersböck M, Brunetti M, Nanni T, Maugeri M, Briffa K, Jones P, Efthymiadis D, Mestre O, Moisselin J-M, Begert M, Brazdil R, Bochnicek O, Cegnar T, Gajić-Čapka M, Zaninović K, Majstorović, Ž, Szalai S, Szentimrey T, Mercalli L (2005) A new instrumental precipitation dataset for the greater alpine region for the period 1800–2002. Int J Climatol 25(2):139–166 CrossRefGoogle Scholar
  6. Boissonnade AC, Heitkemper LJ, Whitehead D (2002) Weather data: cleaning and enhancement. In: Dischel RS (ed) Climate risk and the weather market: financial risk management with weather hedges. Risk Waters, pp 73–93 Google Scholar
  7. Buishand TA (1982) Some methods for testing the homogeneity of rainfall records. J Hydrol 58:11–27 CrossRefGoogle Scholar
  8. Carvalho J, Delgado-García J, Soares A (2006) Merging Landsat and SPOT digital data using stochastic simulation with reference images. In: Caetano M, Painho M (eds) Proceedings of accuracy 2006 – 7th international symposium on spatial accuracy assessment in natural resources and environmental sciences. Instituto Geográfico Português, pp 567–577 Google Scholar
  9. Costa AC, Soares A (2006) Identification of inhomogeneities in precipitation time series using SUR models and the Ellipse test. In: Caetano M, Painho M (eds) Proceedings of accuracy 2006 – 7th international symposium on spatial accuracy assessment in natural resources and environmental sciences. Instituto Geográfico Português, pp 419–428 Google Scholar
  10. Costa AC, Negreiros J, Soares A (2008) Identification of inhomogeneities in precipitation time series using stochastic simulation. In: Soares A, Pereira MJ, Dimitrakopoulos R (eds) geoENV VI – Geostatistics for Environmental Applications. Springer, Berlin, pp 275–282 CrossRefGoogle Scholar
  11. Craddock JM (1979) Methods of comparing annual rainfall records for climatic purposes. Weather 34:332–346 Google Scholar
  12. Ducré-Robitaille J-F, Vincent LA, Boulet G (2003) Comparison of techniques for detection of discontinuities in temperature series. Int J Climatol 23(9):1087–1101 CrossRefGoogle Scholar
  13. Easterling DR, Peterson TC (1995) A new method for detecting and adjusting for undocumented discontinuities in climatological time series. Int J Climatol 15:369–377 CrossRefGoogle Scholar
  14. Feng S, Hu Q, Qian W (2004) Quality control of daily meteorological data in China, 1951–2000: a new dataset. Int J Climatol 24(7):853–870 CrossRefGoogle Scholar
  15. Hirsch RM, Slack JR (1984) A nonparametric trend test for seasonal data with serial dependence. Water Resour Res 20(6):727–732 CrossRefGoogle Scholar
  16. Hirsch RM, Slack JR, Smith RA (1982) Techniques of trend analysis for monthly water quality data. Water Resour Res 18(1):107–121 CrossRefGoogle Scholar
  17. Horta A, Soares A (2008) Data integration model for soil degradation risk assessment. In: Proceedings of the 8th international geostatistics congress, 1–5 December 2008, Santiago, Chile Google Scholar
  18. Kendall MG (1975) Rank correlation methods. Charles Griffin, London Google Scholar
  19. Kohler MA (1949) Double-mass analysis for testing the consistency of records and for making adjustments. Bull Am Meteorol Soc 30:188–189 Google Scholar
  20. Kruskal WH (1952) A nonparametric test for the several sample problem. Ann Math Stat 23:525–540 CrossRefGoogle Scholar
  21. Kruskal WH, Wallis WA (1952) Use of ranks in one-criterion variance analysis. J Am Stat Assoc 47:583–621 CrossRefGoogle Scholar
  22. Kyriakidis PC, Miller NL, Kim J (2004) A spatial time series framework for simulating daily precipitation at regional scales. J Hydrol 297:236–255 CrossRefGoogle Scholar
  23. Lettenmaier DP (1988) Multivariate nonparametric tests for trend in water quality. Water Resour Bull 24(3):505–512 Google Scholar
  24. Libiseller C, Grimvall A (2002) Performance of partial Mann-Kendall test for trend detection in the presence of covariates. Environmetrics 13:71–84 CrossRefGoogle Scholar
  25. Mann HB (1945) Non-parametric test against trend. Econometrika 13:245–259 CrossRefGoogle Scholar
  26. Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 18:50–60 CrossRefGoogle Scholar
  27. Menne MJ, Williams CN (2005) Detection of undocumented changepoints using multiple test statistics and composite reference series. J Climate 18(20):4271–4286 CrossRefGoogle Scholar
  28. Peterson TC, Easterling DR, Karl TR, Groisman P, Nicholls N, Plummer N, Torok S, Auer I, Boehm R, Gullett D, Vincent L, Heino R, Tuomenvirta H, Mestre O, Szentimrey T, Salinger J, Forland EJ, Hanssen-Bauer I, Alexandersson H, Jones P, Parker D (1998) Homogeneity adjustments of in situ atmospheric climate data: A review. Int J Climatol 18(13):1493–1517 CrossRefGoogle Scholar
  29. Pettit AN (1979) A non-parametric approach to the change-point detection. Appl Statist 28(2):126–135 CrossRefGoogle Scholar
  30. Potter KW (1981) Illustration of a new test for detecting a shift in mean in precipitation series. Mon Weather Rev 109:2040–2045 CrossRefGoogle Scholar
  31. Reeves J, Chen J, Wang XL, Lund R, Lu Q (2007) A review and comparison of changepoint detection techniques for climate data. J Appl Meteorol Clim 46:900–915 CrossRefGoogle Scholar
  32. Romero R, Guijarro JA, Ramis C, Alonso S (1998) A 30-year (1964–1993) daily rainfall data base for the Spanish Mediterranean regions: First exploratory study. Int J Climatol 18(5):541–560 CrossRefGoogle Scholar
  33. Soares A (2001) Direct sequential simulation and cosimulation. Math Geol 33(8):911–926 CrossRefGoogle Scholar
  34. Solow A (1987) Testing for climatic change: an application of the two phase regression model. J Clim Appl Meteorol 26:1401–1405 CrossRefGoogle Scholar
  35. Szentimrey T (1994) Statistical problems connected with the homogenization of climatic time series. In: Heino R (ed) Climate variations in Europe. Proceedings of the European workshop held in Kirkkonummi (Majvik), Finland, May 1994, Publications of the Academy of Finland, pp 330–339 Google Scholar
  36. Szentimrey T (1999) Multiple analysis of series for homogenization (MASH). In: Proceedings of the second seminar for homogenization of surface climatological data. Budapest, Hungary, WMO-TD No. 962, WCDMP No. 41, pp 27–46 Google Scholar
  37. Szentimrey T (2003) Multiple analysis of series for homogenization (MASH); Verification procedure for homogenized time series. In: Fourth seminar for homogenization and quality control in climatological databases. Budapest, Hungary, WMO-TD No. 1236, WCDMP No. 56, pp 193–201 Google Scholar
  38. Tayanç M, Dalfes HN, Karaca M, Yenigün O (1998) A comparative assessment of different methods for detecting inhomogeneities in Turkish temperature data set. Int J Climatol 18(5):561–578 CrossRefGoogle Scholar
  39. Teegavarapu RSV, Chandramouli V (2005) Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records. J Hydrol 312:191–206 CrossRefGoogle Scholar
  40. Tuomenvirta H (2001) Homogeneity adjustments of temperature and precipitation series – Finnish and Nordic data. Int J Climatol 21(4):495–506 CrossRefGoogle Scholar
  41. Vincent L (1998) A technique for the identification of inhomogeneities in Canadian temperature series. J Climate 11:1094–1104 CrossRefGoogle Scholar
  42. Von Neumann J (1941) Distribution of the ratio of the mean square successive difference to the variance. Ann Math Stat 13:367–395 CrossRefGoogle Scholar
  43. Wald A, Wolfowitz J (1943) An exact test for randomness in the non-parametric case based on serial correlation. Ann Math Stat 14:378–388 CrossRefGoogle Scholar
  44. Wijngaard J, Klein Tank AMG, Können GP (2003) Homogeneity of 20th century European daily temperature and precipitation series. Int J Climatol 23(6):679–692 CrossRefGoogle Scholar
  45. Wilcoxon F (1945) Individual comparison by ranking methods. Biometrics 1:80–83 CrossRefGoogle Scholar
  46. Yue S, Wang CY (2004) The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Resour Manag 18(3):201–218 CrossRefGoogle Scholar

Copyright information

© International Association for Mathematical Geosciences 2008

Authors and Affiliations

  1. 1.ISEGIUniversidade Nova de LisboaLisboaPortugal
  2. 2.CERENAInstituto Superior TécnicoLisboaPortugal

Personalised recommendations