Software
The software packages for data homogeneization (RHtestsV3) and indices calculation (RClimDex) are based on a very powerful and freely available statistical package R which runs under both MicroSoft Windows and Unix/Linux. Please see the Quick Guide below to download and install R:
Data homogenization
This RHtestsV3 software package can be used to detect, and adjust for, multiple changepoints (shifts) that could exist in a data series that may have first order autoregressive errors [but excluding daily precipitation data series, for which the RHtests_dlyPrcp package also given below) should be used]. It is based on the penalized maximal t test (Wang et al. 2007) and the penalized maximal F test (Wang 2008b), which are embedded in a recursive testing algorithm (Wang 2008a), with the lag-1 autocorrelation (if any) of the time series being empirically accounted for. The problem of uneven distribution of false alarm rate and detection power is also greatly alleviated by using empirical penalty functions (Wang et al. 2007, Wang 2008b). The time series being tested may have zero-trend or a linear trend throughout the whole period of record. A homogenous time series that is well correlated with the base series may be used as a reference series. However, detection of changepoints is also possible with the RHtestsV3 package when a homogenous reference series is not available.
The RHtestsV3 package is an extended version of the RHtestV2 package. The extension includes: (1) provision of Quantile-Matching (QM) adjustments ( Wang et al. 2010, section 5 ) in addition to the mean-adjustments that were provided in the RHtestV2; (2) choice of the segment to which the base series is to be adjusted (referred to as the base segment); (3) choices of the nominal level of confidence at which to conduct the test; and (4) all functions are now available in the GUI mode. This package has been developed and maintained by Xiaolan Wang and Yang Feng at Climate Research Division. Equivalent FORTRAN programs are also available by sending a request in English to Xiaolan Wang.
- RHtestsV3 Software
- RHtestsV3 User Manual
- RHtestsV3 User Manual - French version
- RHtestsV3 Example One Data
- RHtestsV3 Example Two Data and Reference series
- Description of the QM adjustment algorithm (Wang et al. 2010, section 5)
- Table of empirical percentiles of the PMT test statistic (included in the codes)
- Table of empirical percentiles of the PMF test statistic (included in the codes)
- Quick Guide to RClimDex and RHtests Users
- Quick Guide to RClimDex and RHtests Users - French version
- RClimDex-RHtests data format conversion software
- Software for homogenization of daily precipitation data series (Wang et al. 2010) and Example Data
Indices calculation
RClimDex
The RClimDex provides a friendly graphical user interface to compute all 27 core indices . It also conducts simple quality control on the input daily data. It has been developed and maintained by Xuebin Zhang and Yang Feng at Climate Research Division. The software was used first at the South Africa Workshop in Cape Town, South Africa, in June 2004 and has been used in other ET workshops .- RClimDex Software Version 1.0 (updated 06/18/2008)
- RClimDex Users Guide
- RClimDex Users Guide in Spanish, translated by José Luis Santos of CIIFEN
- RClimDex Example Data
- RClimDex Example Data1
FClimDex
The FClimDex is a FORTRAN program that conducts data quality control and computes all the indices. Note that a FORTRAN 90 compiler is required to use this program.ClimDex
An older MicroSoft Excel based indices software ClimDex, developed by ( Byron Gleason of the U.S. National Climatic Data Centre is still available. This software was used at the Caribbean Regional Climate Change workshop was held in Kingston, Jamaica in January 2001. Note that this software does not include recent improvements recommended by ET.- ClimDex Software Version 1.3
- ClimDex Users Guide in English
- ClimDex Users Guide in Portuguese
- ClimDex Example Data for De Bilt, The Netherlands
Extreme value modelling
- Stuart Coles S-Plus functions and their R implementation at NCAR

