This page lists break detection methods used for the homogenisation of climate data. The correction methods are a separated category. Methods doing both detection and correction are in this list. The first list was based on the HOME bibliography maintained by Alba Gilabert. This bibliography also lists methods proposed in the mathematical literature and many application papers.
This list is currently just a mock-up to show how it would look like. Once we have review pages to link to, we could shorten the citation and give the full citation on the review pages.
- Domonkos, P. and J. Coll, 2017: Homogenisation of temperature and precipitation time series with ACMANT3: method description and efficiency tests. Int. J. Climatol., 37, 1910–1921. doi: 10.1002/joc.4822.
- ACMANT is probably the best automatic homogenisation method for temperature we currently have.
- Caussinus, H. and O. Mestre, 2004: Detection and correction of artificial shifts in climate series. Journal of the Royal Statistical Society Series C-Applied Statistics, 53, 405-425.
- PRODIGE is one of the best manual homogenisation methods. It is a pairwise method, now implemented as part of HOMER.
- Menne, M.J. and C.N. Williams, 2009: Homogenization of Temperature Series via Pairwise Comparisons. J. Climate, 22, 1700–1717, doi: 10.1175/2008JCLI2263.1
- Important because GHCN is homogenised with this method and it is the only method validated in HOME that can handle global datasets.
- Alexandersson, H., 1986: A homogeneity test applied to precipitation data. Int. J. Climatol., 6, 661-675.
- For historical interest.
- 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.
- For historical interest.
To be assessed
Below we list the articles we still have to assess (or have to decide they are outside of the scope of this journal). I wonder whether it would not look better to keep this list internal. Is a list of unreviewed article a useful service? It will stay a long list of unreviewed articles for a long time.
Alexandersson, H. and U. u. M. institutionen, 1984: A Homogeneity Test Based on Ratios and Applied to Precipitation Series. 55 pp., Meteorologiska Institutionen, Kungl. Universitetet, Uppsala, Uppsala.
Alexandersson, H. and A. Moberg, 1997: Homogenization of Swedish temperature data. Homogeneity test for linear trends. Int. J. Climatol., 17, 25-34.
Allen, R.J. and A.T. DeGaetano, 2000: A method to adjust long-term temperature extreme series for nonclimatic inhomogeneities. J. Clim., 13, 3680-3695.
Beaulieu, C., 2009: Homogenization of precipitation series. INRS-ETE, Quebec, 1-309.
Beaulieu, C., T.B.M.J. Ouarda, and O. Seidou, 2010: A Bayesian Normal Homogeneity Test for the detection of artificial discontinuities in climatic series. Int. J. Climatol., 30, 2342–2357.
Beersma, J.J. and T.A. Buishand, 1999: A simple test for equality of variances in monthly climate data. J. Clim., 12, 1770-1779.
Begert, M., E. Zenzklusen, C. Haberli, C. Appenzeller, and L. Klok, 2008: An automated procedure to detect discontinuities; performance assessment and application to a large European climate data set. Meteorol. Z., 17, 663-672.
Besse, P.C. and N. Raimbault, 2006: Comparisons of split linear fitting of wind curves. Journal of Data Science, 4, 497-509.
Ray, Bonnie K. and Ruey S. Tsay, 2002: Bayesian methods for change-point detection in long-range dependent processes. Journal of Time Series Analysis, 23, 687-705.
Boulanger, J.P., J. Aizpuru, L. Leggieri, and M. Marino, 2009: A procedure for automated quality control and homogenization of historical daily temperature and precipitation data (APACH): part 1: quality control and application to the Argentine weather service stations. Climatic Change, 98, 471-491.
Brandsma, T. and G.P. Konnen, 2006: Application of nearest-neighbor resampling for homogenizing temperature records on a daily to sub-daily level. Int. J. Climatol., 26, 75-89.
Brandsma, T. and J.P. van der Meulen, 2008: Thermometer screen intercomparison in De Bilt (the Netherlands) – Part II: Description and modeling of mean temperature differences and extremes. Int. J. Climatol., 28, 389–400. doi: 10.1002/joc.1524.
Brunetti, M., M. Maugeri, F. Monti, and T. Nanni, 2006: The variability and change of Italian climate in the last 160 years. Nuovo Cimento Della Societa Italiana Di Fisica C-Geophysics and Space Physics, 29, 3-12.
Buishand, T.A. and J.J. Beersma, 1996: Statical test for comparison of daily variability in observed and simulated climates. J. Clim., 9, 2538-2550.
Buishand, T.A., 1982: Some methods for testing the homogeneity of rainfall records. J. hydrol., 58, 11-27.
Caesar, J., L. Alexander, and R. Vose, 2006: Large-scale changes in observed daily maximum and minimum temperatures: Creation and analysis of a new gridded data set. Journal of Geophysical Research-Atmospheres, 111, 10.
Chopin, N., 2007: Dynamic detection of change points in long time series. Annals of the Institute of Statistical Mathematics, 59, 349-366.
Christy, J.R., W.B. Norris, K. Redmond, and K.P. Gallo, 2006: Methodology and results of calculating central California surface temperature trends: Evidence of human-induced climate change? J. Clim., 19, 548-563.
Cioranescu, D. and P. Donato, 1999: An Introduction to Homogenization. Oxford Lecture Series in Mathematics and its Applications, ix, 262 p. pp., Oxford University Press, Oxford, New York.
Conrad, V., 1925: Homogenitätsbestimmung meteorologischer Beobachtungsreihen. Meteorol. Z., 42, 482–485.
Costa, A.C. and A. Sores, 2009: Homogenization of Climate Data: Review and New Perspectives Using Geostatistics. Math Geosci, 41, 291-305, doi: 10.1007/s11004-008-9203-3.
Datsenko, N.M., A. Moberg, and D.M. Sonechkin, 2002: Objective time-scale-dependent homogenization of early instrumental temperature series. Theoretical and Applied Climatology, 72, 103-126.
Domonkos, P., 2014: The ACMANT2 software package. Eighth Seminar for Homogenization and Quality Control in Climatological Databases and Third Conference on Spatial Interpolation Techniques In Climatology and Meteorology. WMO, WCDMP-84, 46-72.
Domonkos, P., 2011: Adapted Caussinus-Mestre Algorithm for homogenising Networks of Temperature series (ACMANT). Int. J. Geosci., 2, 293–309, doi: 10.4236/ijg.2011.23032.
Domonkos, P., Poza, R., and Efthymiadis, D., 2011: Newest developments of ACMANT. Adv. Sci. Res., 6, 7–11, doi: 10.5194/asr-6-7-2011.
Domonkos, P., 2015: Homogenization of precipitation time series with ACMANT. Theor. Appl. Climatol., 122, 303-314. doi: 10.1007/s00704-014-1298-5.
Efthymiadis, D., P.D. Jones, K.R. Briffa, I. Auer, R. Bohm, W. Schoner, C. Frei, and J. Schmidli, 2006: Construction of a 10-min-gridded precipitation data set for the Greater Alpine Region for 1800-2003. Journal of Geophysical Research-Atmospheres, 111, 22.
Gey, S. and E. Lebarbier, 2000: Using CART to detect changepoints in the mean.
Gruber, C. and L. Haimberger, 2008: On the homogeneity of radiosonde wind time series. Meteorol. Z., 17(5), 631-643, doi: 10.1127/0941-2948/2008/0298.
Haimberger, L., 2007: Homogenization of radiosonde temperature time series using innovation statistics. J. Clim., 20, 1377-1403.
Haimberger, L. and European Centre for Medium Range Weather Forecasts, 2005: Homogenization of Radiosonde Temperature Time Series using ERA-40 Analysis Feedback Information. ERA-40 Project Report Series, 68 p. pp., European Centre for Medium-Range Weather Forecasts, [Reading, England].
Heidke, P., 1923: Quantitative Begriffsbestimmung homogener Temperatur- und Niederschlagsreihen. Meteorol. Z., 40, 114–115.
Helmert, F.R., 1907: Die Ausgleichrechnung nach der Methode der kleinsten Quadrate. 2. Auflage. Teubner Verlag.
Hogrefe, C., S.T. Rao, and I.G. Zurbenko, 1998: Detecting trends and biases in time series of ozonesonde data. Atmos. Environ., 32(14/15), 2569-2586.
Karl, T.R. and C.N. Williams, 1987: An approach to adjusting Climatological Time Series for discontinuous inhomogeneities. Journal of climate an applied meteorology, 26, 1744-1763.
Khaliq, M.N. and T.B.M.J. Ouarda, 2007: On the critical values of the standard normal homogeneity test (SNHT). Int. J. Climatol., 27, 681-687.
Kirono, D.G.C. and R.N. Jones, 2007: A bivariate test for detecting inhomogeneities in pan evaporation time series. Aust. Meteorol. Mag., 56, 93-103.
Kohler, M.A., 1949: Double-mass analysis for testing the consistency of records and for making adjustments. B. Am. Meteorol. Soc., 30, 188–189.
Lanzante, J.R., 1996: Resistant, robust and non-parametric techniques for the analysis of climate data: Theory and examples, including applications to historical radiosonde station data. Int. J. Climatol., 16, 1197-1226.
Lanzante, J.R., S.A. Klein, and D.J. Seidel, 2003: Temporal homogenization of monthly radiosonde temperature data. Part I: Methodology. J. Clim., 16, 224-240.
Lanzante, J.R., S.A. Klein, and D.J. Seidel, 2003: Temporal homogenization of monthly radiosonde temperature data. Part II: Trends, sensitivities, and MSU comparison. J. Clim., 16, 241-262.
Li, Q. and W. Dong, 2009: Detection and Adjustment of Undocumented Discontinuities in Chinese Temperature Series Using a Composite Approach. Advances in atmospheric sciences, 26, 143-153.
Lund, R. and J. Reeves, 2002: Detection of Undocumented Changepoints: A Revision of the Two-Phase Regression Model. J. Clim., 15, 2547-2554.
Lund, R., X.L. Wang, Q.Q. Lu, J. Reeves, C. Gallagher, and Y. Feng, 2007: Changepoint detection in periodic and autocorrelated time series. J. Clim., 20, 5178-5190.
Maugeri, M., M. Brunetti, F. Monti, and T. Nanni, 2003: Sea-level pressure variability in the Po Plain (1765-2000) from homogenized daily secular records. Int. J. Climatol., 24, 437-455.
Mitchell, T.D. and P.D. Jones, 2005: An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J. Climatol., 25, 693-712.
Moreno, E., G. Casella, and A. Garcia-Ferrer, 2005: An objective Bayesian analysis of the change point problem. Stochastic Environmental Research and Risk Assessment, 19, 191-204.
Perreault, L., E. Parent, J. Bernier, B. Bobee, and M. Slivitzky, 2000: Retrospective multivariate Bayesian change-point analysis: A simultaneous single change in the mean of several hydrological sequences. Stochastic Environmental Research and Risk Assessment, 14, 243-261.
Peterson, T.C. and D.R. Easterling, 1994: Creation of homogeneous composite climatological reference series. Int. J. Climatol., 14, 671-679.
Peterson, T.C., T.R. Karl, P.F. Jamason, R. Knight, and D.R. Easterling, 1998: First difference method: Maximizing station density for the calculation of long-term global temperature change. Journal of Geophysical Research-Atmospheres, 103, 25967-25974.
Peterson, T. C., R. Vose, R. Schmoyer, and V. Razuvaev, 1998: Global historical climatology network (GHCN) quality control of monthly temperature data. Int. J. Climatol., 18, 1169-1179.
Seidou, O., J. J. Asselin, and T. Ouarda, 2007: Bayesian multivariate linear regression with application to change point models in hydrometeorological variables. Water Resour. Res., 43, doi: 10.1029/2005WR004835.
Seidou, O. and T. Ouarda, 2007: Recursion-based multiple changepoint detection in multiple linear regression and application to river streamflows. Water Resour. Res., 43.
Sherwood, S.C., 2007: Simultaneous detection of climate change and observing biases in a network with incomplete sampling. J. Clim., 20, 4047-4062.
Son, Y.S. and S.W. Kim, 2005: Bayesian single change point detection in a sequence of multivariate normal observations. Statistics, 39, 373-387.
Staudt, M., M.J. Esteban-Parra, and Y. Castrodiez, 2006: Homogenization of long-term monthly Spanish temperature data. Int. J. Climatol.
Szentimrey, T., 1999: Multiple Analysis of Series for homogenization (MASH). Proceedings of the second seminar for homogenization of surface climatological data. Budapest, Hungary, WMO, WCDMP-No. 41, 27–46.
Szentimrey, T., 2007: Manual of homogenization software MASHv3.02. Hungarian Meteorological Service, p. 65.
Terence Tai-Leung Chong, 2003: Generic consistency of the break-point estimator under specification errors. Econometrics Journal, 6, 167-192.
Tomozeiu, R., A. Busuioc, V. Marletto, F. Zinoni, and C. Cacciamani, 2000: Detection of changes in the summer precipitation time series of the region Emilia-Romagna, Italy. Theoretical and Applied Climatology, 67, 193-200.
Toreti, A., F.G. Kuglitsch, E. Xoplaki, P.M. Della-Marta, E. Aguilar, and M.a.J.L. Prohomf, 2010: A note on the use of the standard normal homogeneity test to detect inhomogeneities in climatic time series. Int. J. Climatol., 1-3.
Toumenvirta, H., 2000: Homogeneity adjustments of temperature and precipitation series – Finnish and nordic data. Int. J. Climatol., 21, 495-506.
Tuomenvirta, H., 2001: Homogeneity adjustments of temperature and precipitation series – Finnish and Nordic data. Int. J. Climatol., 21, 495-506.
Venter, J.H. and S.J. Steel, 1996: Finding multiple abrupt change points. Comput. Stat. Data Anal., 22, 481-504.
Vicente-Serrano, S.M., J. Begueria, J.J. López-Moreno, M.A. García-Vera, and P. Stepanek, 2008: A complete daily precipitation database for northeast Spain: reconstruction, quality control, and homogeneity. Int. J. Climatol., 30, 1146–1163, doi: 10.1002/joc.1850.
Vincent, L.A., 1997: A Technique for the Identification of Inhomogeneities in Canadian Temperature Series. J. Clim., 11, 1904-1104.
Vincent, L.A. and D.W. Gullett, 1999: Canadian historical and homogeneous temperature datasets for climate change analyses. Int. J. Climatol., 19, 1375-1388.
Wang, X.L., 2008: Accounting for Autocorrelation in Detecting Mean Shifts in Climate Data Series Using the Penalized Maximal t or F Test. J. Appl. Meteorol. Climatol., 47, 2423-2444.
Wijngaard J.B., A.M.G. Klein-Tank, G.P. Könen, 2003: Homogeneity of 20th century European daily temperature and precipitation series. International Journal of Climatology, 23 (6): 679-692.
Wulfmeyer, V. and I. Henning-Muller, 2006: The climate station of the University of Hohenheim: Analyses of air temperature and precipitation time series since 1878. Int. J. Climatol., 26, 113-138.
Wang, X.L., 2007: Penalized maximal F-test for detecting undocumented mean-shift without trend-change. J. Atmos. Ocean. Tech., 05.