README.md
In SvenKotlarski/qmCH2018: Quantile Mapping for CH2018
qmCH2018
What is qmCH2018?
qmCH2018 is an implementation of quantile mapping (QM) based on empirical quantiles. The package for the R programming language has been developed at ETH Zurich and MeteoSwiss and is used for bias-correcting and downscaling EURO-CORDEX RCM output within the frame of the CH2018 Swiss Climate Change Scenarios.
Installation
The recommended procedure for installing the package is using the devtools package.
devtools::install_github("SvenKotlarski/qmCH2018")
Check the options of the main (wrapper) function for the application of the QM:
library(qmCH2018)
?qm.doqm
Reference
Users are required to reference the following publication:
- Rajczak, J. , Kotlarski, S. , Salzmann, N. and Schär, C. (2016), Robust climate scenarios for sites with sparse observations: a two‐step bias correction approach. Int. J. Climatol., 36: 1226-1243. doi:10.1002/joc.4417
License
GPLv3 (https://www.gnu.org/licenses/gpl-3.0.de.html)
DOI
10.5281/zenodo.3275571
SvenKotlarski/qmCH2018 documentation built on July 14, 2019, 7:39 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
qmCH2018
What is qmCH2018?
qmCH2018 is an implementation of quantile mapping (QM) based on empirical quantiles. The package for the R programming language has been developed at ETH Zurich and MeteoSwiss and is used for bias-correcting and downscaling EURO-CORDEX RCM output within the frame of the CH2018 Swiss Climate Change Scenarios.
Installation
The recommended procedure for installing the package is using the devtools package.
devtools::install_github("SvenKotlarski/qmCH2018")
Check the options of the main (wrapper) function for the application of the QM:
library(qmCH2018)
?qm.doqm
Reference
Users are required to reference the following publication:
- Rajczak, J. , Kotlarski, S. , Salzmann, N. and Schär, C. (2016), Robust climate scenarios for sites with sparse observations: a two‐step bias correction approach. Int. J. Climatol., 36: 1226-1243. doi:10.1002/joc.4417
License
GPLv3 (https://www.gnu.org/licenses/gpl-3.0.de.html)
DOI
10.5281/zenodo.3275571
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.