Skip to main content
Log in

Homogenization of Climate Data: Review and New Perspectives Using Geostatistics

  • Special Issue
  • Published:
Mathematical Geosciences Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • 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

  • Alexandersson H (1986) A homogeneity test applied to precipitation data. J Climatol 6:661–675

    Article  Google Scholar 

  • Alexandersson H, Moberg A (1997) Homogenization of Swedish temperature data. Part I: Homogeneity test for linear trends. Int J Climatol 17(1):25–34

    Article  Google Scholar 

  • 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

  • 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

    Article  Google Scholar 

  • 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

  • Buishand TA (1982) Some methods for testing the homogeneity of rainfall records. J Hydrol 58:11–27

    Article  Google Scholar 

  • 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

  • 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

  • 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

    Chapter  Google Scholar 

  • Craddock JM (1979) Methods of comparing annual rainfall records for climatic purposes. Weather 34:332–346

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Hirsch RM, Slack JR (1984) A nonparametric trend test for seasonal data with serial dependence. Water Resour Res 20(6):727–732

    Article  Google Scholar 

  • Hirsch RM, Slack JR, Smith RA (1982) Techniques of trend analysis for monthly water quality data. Water Resour Res 18(1):107–121

    Article  Google Scholar 

  • 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

  • Kendall MG (1975) Rank correlation methods. Charles Griffin, London

    Google Scholar 

  • 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 

  • Kruskal WH (1952) A nonparametric test for the several sample problem. Ann Math Stat 23:525–540

    Article  Google Scholar 

  • Kruskal WH, Wallis WA (1952) Use of ranks in one-criterion variance analysis. J Am Stat Assoc 47:583–621

    Article  Google Scholar 

  • Kyriakidis PC, Miller NL, Kim J (2004) A spatial time series framework for simulating daily precipitation at regional scales. J Hydrol 297:236–255

    Article  Google Scholar 

  • Lettenmaier DP (1988) Multivariate nonparametric tests for trend in water quality. Water Resour Bull 24(3):505–512

    Google Scholar 

  • Libiseller C, Grimvall A (2002) Performance of partial Mann-Kendall test for trend detection in the presence of covariates. Environmetrics 13:71–84

    Article  Google Scholar 

  • Mann HB (1945) Non-parametric test against trend. Econometrika 13:245–259

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Menne MJ, Williams CN (2005) Detection of undocumented changepoints using multiple test statistics and composite reference series. J Climate 18(20):4271–4286

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Pettit AN (1979) A non-parametric approach to the change-point detection. Appl Statist 28(2):126–135

    Article  Google Scholar 

  • Potter KW (1981) Illustration of a new test for detecting a shift in mean in precipitation series. Mon Weather Rev 109:2040–2045

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Soares A (2001) Direct sequential simulation and cosimulation. Math Geol 33(8):911–926

    Article  Google Scholar 

  • Solow A (1987) Testing for climatic change: an application of the two phase regression model. J Clim Appl Meteorol 26:1401–1405

    Article  Google Scholar 

  • 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

  • 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

  • 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

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Tuomenvirta H (2001) Homogeneity adjustments of temperature and precipitation series – Finnish and Nordic data. Int J Climatol 21(4):495–506

    Article  Google Scholar 

  • Vincent L (1998) A technique for the identification of inhomogeneities in Canadian temperature series. J Climate 11:1094–1104

    Article  Google Scholar 

  • Von Neumann J (1941) Distribution of the ratio of the mean square successive difference to the variance. Ann Math Stat 13:367–395

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Wilcoxon F (1945) Individual comparison by ranking methods. Biometrics 1:80–83

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ana Cristina Costa.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Costa, A.C., Soares, A. Homogenization of Climate Data: Review and New Perspectives Using Geostatistics. Math Geosci 41, 291–305 (2009). https://doi.org/10.1007/s11004-008-9203-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11004-008-9203-3

Keywords

Navigation