`Rglimclim`

: A multisite, multivariate weather generator based on generalised linear models`R`

, which is freely available from the CRAN link at http://www.R-project.org. It runs under any operating system that can run `R`

. The package is supplied as a precompiled binary distribution for `Windows`

users, and as a source package for other operating systems. Download the latest version for your operating system here:
**Under Windows**:- Download the appropriate
`zip`

archive from the link above, and save it to your computer. Start up`R`

, ensuring that you have administrative privileges (you may need to right-click on the`R`

desktop icon and select "Run as administrator"). Then, from the`Packages`

menu, select`Install package(s) from local zip files`

: the installation process should be straightforward from here. **Under Unix**:- The package must be compiled from its source code which is supplied
as a compressed tarball. Download and save this from the link above.
Next, open a terminal and navigate to the directory where you saved the
tarball. The package can now be installed from the Unix prompt using

`R CMD INSTALL --html --clean Rglimclim`

You will need administrative privileges for this. Precise details are implementation-dependent (use`sudo`

on Ubuntu systems, for example).The command above will ensure that HTML help files are installed and that the installation cleans up after itself. To see the other installation options that are available, type

`R CMD INSTALL --help`

. **Other operating systems**:-
Users of other operating systems should build the package from the tarball. If unsure how to do this, read the
`R`

documentation (start up`R`

, type`help.start()`

and follow the `R Installation and Administration' link).

`R`

by typing `require(Rglimclim)`

at the prompt. For an overview of the package, type `help("Rglimclim-package")`

. This should bring up a help page in a web browser (if it doesn't, type `help("Rglimclim-package",help_type="html")`

). this help page gives a brief overview of the package; at the bottom is a link to the `PDF`

package manual, which should be the starting point for all new users.
In particular, Chapter 5 of the manual provides a worked example to
demonstrate the capabilities of the software.
`PDF`

package manual contains an extensive appendix
detailing the theory underlying the software package. Parts of this
theory have been published elsewhere, including in the following
references:
- Chandler, R.E. and Wheater, H.S. (2002). Analysis of rainfall variability using generalized linear
models: a case study from the west of Ireland.
*Water Resources Research***38(10)**, 1192, doi:10.1029/2001WR000906. - Yan, Z., Bate, S., Chandler, R.E, Isham, V.S. and Wheater,H.S.(2002):
An analysis of daily maximum windspeed in northwestern Europe using
Generalized Linear Models. J. Climate,
**15, no.15**, pp. 2073-2088. - Yan, Z., Bate, S., Chandler, R.E., Isham, V. and Wheater, H. (2006): Changes in extreme
wind speeds in NW Europe simulated by generalized linear models.
*Theoretical and Applied Climatology***83**, pp. 121-137. doi:10.1007/s00704-005-0156-x. - Yang, C., Chandler, R.E., Isham, V. and Wheater, H.S. (2005). Spatial-temporal rainfall
simulation using Generalized Linear Models.
*Water Resources Research***41**, doi:10.1029/2004WR003739. - Chandler, R.E. and Bate, S. (2007).
Inference for clustered data using the independence log-likelihood.
*Biometrika***94**, pp. 167-183. doi:0.1093/biomet/asm015. - Ambrosino, C., R.E. Chandler and M.C. Todd (2014).
Rainfall-derived growing season characteristics for agricultural impact
assessments in South Africa.
*Theoretical and Applied Climatology*,**115**, 411-426, doi: 10.1007/s00704-013-0896-y.

`Rglimclim`

and its predecessor (see below) have been used in
several case studies. The publications below include some that I'm
aware of. If you have used it and would like your paper to be listed
here, please email me!.
- Chun, K.P., S.D. Mamet, J. Metsaranta, A. Barr, J. Johnstone and H.
Wheater (2017). A novel stochastic method for reconstructing daily
precipitation times-series using tree-ring data from the western
Canadian Boreal Forest.
*Dendrochronologia*,**44**, 9-18, doi: 10.1016/j.dendro.2017.01.003. - Asong, Z.E., M.N. Khaliq and H.S. Wheater (2016). Multisite
multivariate modeling of daily precipitation and temperature in the
Canadian Prairie Provinces using generalized linear models.
*Climate Dynamics*,**47**, 2901-2921, doi: 10.1007/s00382-016-3004-z. - Asong, Z.E., M.N. Khaliq and H.S. Wheater (2016). Projected
changes in precipitation and temperature over the Canadian Prairie
Provinces using the Generalized Linear Model statistical downscaling
approach.
*Journal of Hydrology*,**539**, 429-446, doi: 10.1016/j.jhydrol.2016.05.044. - Mockler, E.M., K.P. Chun, G. Sapriza-Azuri, M. Bruen and H.S.
Wheater (2016). Assessing the relative importance of parameter and
forcing uncertainty and their interactions in conceptual hydrological
model simulations.
*Advances in Water Resources*,**97**, 299-313, doi:10.1016/j.advwatres.2016.10.008. - Kenabatho, P.K., N.R. McIntyre, R.E. Chandler and H.S. Wheater
(2012). Stochastic simulation of rainfall in the semi-arid Limpopo
basin, Botswana.
*International Journal of Climatology*,**32(7)**, 1113-1127, doi: 10.1002/joc.2323. -
Frost, A.J., S.P. Charles, B. Timbal, F.H.S. Chiew, R. Mehrotra, K.C.
Nguyen, R.E. Chandler, J.L. McGregor, G. Fu, D.G.C. Kirono, E. Fernandez
and D.M. Kent (2011). A comparison of multi-site daily rainfall
downscaling techniques under Australian conditions.
*J. Hydrol*,**408**, 1-18, doi: 10.1016/j.jhydrol.2011.06.021.

`Rglimclim`

evolved from the earlier `Glimclim`

suite of programs written in `Fortran 77`

.
It provides considerably enhanced functionality, including multivariate
modelling and an array of graphical procedures for examining fitted
models and simulation performance. `Glimclim`

is now defunct, therefore. Nonetheless, for its users the final version can be downloaded from here.
Page last updated: 3rd August 2020.