Rglimclim: A multisite, multivariate weather generator based on generalised linear models
This package is designed to fit and simulate Generalised Linear Models to daily climate sequences from a network of sites (e.g. weather stations, or model grid nodes). The programs can be used to analyse historical data, and to provide simulations of future climate scenarios, for example to provide input to climate change impact assessment studies.
The package runs under 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:
Installation
To install the package:
- 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).
Getting started
Once the package is installed, it can be loaded within 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.
References
When using Rglimclim in published work, please acknowledge it by citing the following paper:
- Chandler, R.E. (2020). Multisite, multivariate weather generation based on generalised linear models. Environmental Modelling and Software 134, 104867. doi: 10.1016/j.envsoft.2020.104867.
This paper provides an overview of the methodology. In addition, the 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.
Case studies
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.
Glimclim
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.
To Richard's work page.
Page last updated: 20th October 2020.