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Web site of the Task Team on HOMOGENIZATION
(OPACE2, WMO Commission for Climatology)


(Last update: 19/9/2017)

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Presentation

The path to the provision of Climate Services to the public begins by the acquisition of data and their inclusion in homogeneous and quality controlled climatological series. This Task Team on Homogenization (WMO, Commission for Climatology), co-chaired by Victor Venema and Matthew Menne, was instituted to explore ways, building on the existing work, to identify the best performing, skilled and efficient methods for homogenization and quality control of climatological series. (Full Terms of Reference).


Homogenization packages

Changes in the observing conditions or in the environment of the meteorological stations introduce anomalous perturbations in the data series (inhomogeneities). Therefore, homogenization is of paramount importance to obtain reliable analysis of the variability of climatological series, and there exists an extensive literature about the different methodologies applied so far (comprehensive reviews can be seen in Peterson et al., 1998, Aguilar et al., 2003 and Beaulieu et al., 2008). This allows climatologists to choose their preferred methods for their research, often implementing those methods by themselves. But some of them have made their developments available to other users by preparing and documenting ready to use computer packages and distributing them in the Internet or by other means. This is very interesting for operational climatology units of many National Meteorological and Hydrological Services around the world, that sometimes can lack the needed expertise to make their own developments, or else can devote the required time to other priorities.

The European Science Foundation, by means of the COST Office, funded the Action ES0601 («HOME», Advances in Homogenisation Methods of Climate Series: An Integrated Approach) that enabled the inter-comparison of many participating methodologies. Its final conference was held in Budapest during the last week of October 2011, and the results were summarized by Venema et al. (2011). However, and thanks to the discussions held in the frame of this Action, many methods improved their algorithms, and further work is needed to update the information about their performances. Current efforts include the International Surface Temperature Initiative and the Spanish project MULTITEST.

The following tables summarize the main characteristics of currently available homogenization packages. (The first version was compiled thanks to several developers that replied to a survey, complemented by the information gathered from Venema et al., 2011. Updates have been incorporated from developers or other sources. Corrections or supplemental information can be sent to jguijarrop [AT] aemet.es. Thanks to all for your kind cooperation.)


Package Version License Open source Operating System Program type Primary operation Availability
ACMANT 3.1 Freeware Yes DOS/Windows Executable Automatic http://www.c3.urv.cat/data/ACMANTv3.1.zip
AnClim ProClimDB ? Freeware No Windows Executable Interactive (and automatic) http://www.climahom.eu/
Climatol 3.0 GPL Yes (Most) R package Automatic http://www.climatol.eu/index.html
GAHMDI HOMAD ? GPL Yes (Most) R source R/Fortran Automatic
Interactive
mail to andrea.toreti at giub.unibe.ch
GSIMCLI 0.0.1 GPL Yes (Most) Python Automatic (and interactive) http://iled.github.io/gsimcli/
HOMER 2.6 GPL Yes (Most) R source Interactive http://www.homogenisation.org/
MASH 3.03 Freeware No DOS/Windows Executable Automatic (and interactive) https://www.met.hu/en/omsz/rendezvenyek/homogenization_and_interpolation/software/
ReDistribution Test ? Freeware Yes (Most) R source Interactive mail to predrag.petrovic at hidmet.gov.rs
RHtests 4 Freeware Yes (Most) R source Interactive http://etccdi.pacificclimate.org/software.shtml
USHCN 52i Freeware Yes Some linux versions Fortran source Automatic ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/v3/software/52i/phav52i.tar.gz


Package GUI Time resolution Input format Metadata use Detection method Ref. series selection Detection statistic Climatic variables
ACMANT No Monthly & daily ASCII No Reference Correlation Caussinus-Lyazrhi Temperature and precipitation
AnClim ProClimDB Yes Any ASCII DBF Yes Ref. and pairwise Correlation & distance Several Any
Climatol No Monthly & daily ASCII Yes Reference Distance SNHT Any
GAHMDI
HOMAD
No Monthly
Daily
ASCII Yes Pairwise Correlation New method Any
Temperature
GSIMCLI Yes Monthly & yearly ASCII No Multiple references Correlation & distance User defined Any
HOMER No Monthly ASCII Yes Pairwise Correlation Penalized Likelihood Any
MASH No Monthly & daily ASCII Yes Multiple references Correlation MLR & Hypothesis test Any
ReDistribution Test No Sub-daily ASCII No Distribution None SNHT-like Wind speed and direction
RHtests Yes Monthly & daily ASCII Yes Reference Correlation Penalized max. t & F tests Any
USHCN No Monthly ASCII Yes Pairwise Correlation MLR Temperature


Outputs
Package Correction method Missing data tolerance Max. number of series Homogenized series Corrected outliers Corrected breaks Graphics Documentation
ACMANT ANOVA Very high 100 Yes Yes Yes No User's guide
AnClim ProClimDB Several User defined ? Yes Yes Yes Yes Manuals
Climatol Missing data filling Very high 9999* Yes Yes Yes Yes User's guide
GAHMDI HOMAD ? ? ? Yes No Yes Yes None
GSIMCLI User-defined & missing data filling High 9999* Yes Yes Yes No Manuals
HOMER ANOVA 15 year data ? Yes Yes Yes Yes User's guide
MASH Multiple comparisons 30% 500 Yes Yes Yes Yes User's guide
ReDistribution Test None 10-20% ? No No Detected breaks No None
RHtests Multi-phase regression ? 1 Yes No Yes Yes User's guide
USHCN Multiple comparisons Very high 9999* Yes ? Yes No Plain text notes


Explanatory notes and other characteristics:


References:

Aguilar E, Auer I, Brunet M, Peterson TC, Wieringa J (2003): Guidelines on climate metadata and homogenization. WCDMP-No. 53, WMO-TD No. 1186. World Meteorological Organization, Geneve.

Beaulieu C, Seidou O, Ouarda TBMJ, Zhang X, Boulet G, Yagouti A (2008): Intercomparison of homogenization techniques for precipitation data. Water Resour. Res., 44, 20 pp.

Peterson TC, Easterling DR, Karl TR, Groisman P, Nicholls N, Plummer N, Torok S, Auer I, Böhm R, Gullett D, Vincent L, Heino R, Tuomenvirta H, Mestre O, Szentimrey T, Salinger J, Førland E, Hanssen-Bauer I, Alexandersson H, Jones P, Parker D (1998): Homogeneity Adjustments of 'In Situ' Atmospheric Climate Data: A Review. Int. J. Climatol., 18:1493-1518.

Venema V, Mestre O, Aguilar E, Auer I, Guijarro JA, Domonkos P, Vertacnik G, Szentimrey T, Stepanek P, Zahradnicek P, Viarre J, Müller-Westermeier G, Lakatos M, Williams CN, Menne M, Lindau R, Rasol D, Rustemeier E, Kolokythas K, Marinova T, Andresen L, Acquaotta F, Fratianni S, Cheval S, Klancar M, Brunetti M, Gruber C, Prohom Duran M, Likso T, Esteban P and Brandsma T (2012): Benchmarking homogenization algorithms for monthly data. Clim. of the Past, 8:89-115.