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Appendix B - Useful Resources

Published online by Cambridge University Press:  27 December 2017

Douglas Maraun
Affiliation:
Karl-Franzens-Universität Graz, Austria
Martin Widmann
Affiliation:
University of Birmingham
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Summary

A dynamic version of these pages will be available at www.cambridge.org/climate research. The authors intend to regularly update these resources for the coming years.

Statistical Downscaling Software Packages and Portals

Here we present (in alphabetical order) a selection of open-source software packages and online portals to perform statistical downscaling. This list is not comprehensive. We may have missed useful and important resources. Moreover, the methods listed here are affected by the limitations discussed throughout the book and should thus be selected and applied carefully in a given context.

downscaleR

R package by the Santander Meteorology group containing functions for MOS such as scaling, and different quantile mapping versions, and PP methods such as linear regression, generalised linear models, weather-type-based downscaling and the analog method.

https://github.com/SantanderMetGroup/downscaleR/

ENSEMBLES Downscaling Portal

Online portal of the Santander Meteorology Group to carry out downscaling. The webpage provides different sets of predictor and predictand data (with upload options) and a range of MOS and PP methods.

https://www.meteo.unican.es/downscaling/ensembles

ESD

R package by Rasmus Benestad, Abdelkader Mezghani and Kajsa Parding. The package contains a range of functions to post-process and analyse large data sets (e.g. in NetCDF format) including PP statistical downscaling based on linear regression and temporal disaggregation.

https://github.com/metno/esd

Rglimclim

R package for a conditional multisite, multivariate weather generator based on generalised linear models developed by Richard Chandler.

http://www.ucl.ac.uk/∼ucakarc/work/glimclim.html

MeteoLab

Matlab toolbox of the Santander Meteorology group for statistical analysis and data mining in meteorology, focusing on statistical downscaling methods.

https://meteo.unican.es/trac/MLToolbox

SDSM

Online statistical downscaling tool by Rob Wilby for perfect prognosis and changefactor weather generators.

http://co-public.lboro.ac.uk/cocwd/SDSM/

qmap

R package by Lukas Gudmundsson for quantile mapping.

https://cran.r-project.org/web/packages/qmap/

Programmes and Initiatives

CMIP

The Coupled Model Intercomparison Project (CMIP) defined a framework to conduct and intercompare simulations with coupled atmosphere–ocean GCMs. The most recent phase is CMIP5 (Taylor et al. 2012).

http://cmip-pcmdi.llnl.gov/

CORDEX

Initiative of the World Climate Research Programme (WCRP) to advance and coordinate the science and application of climate downscaling through global partnerships, in particular to generate large high-resolution multimodel ensembles for all regions of the Earth.

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Publisher: Cambridge University Press
Print publication year: 2018

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  • Useful Resources
  • Douglas Maraun, Karl-Franzens-Universität Graz, Austria, Martin Widmann, University of Birmingham
  • Book: Statistical Downscaling and Bias Correction for Climate Research
  • Online publication: 27 December 2017
  • Chapter DOI: https://doi.org/10.1017/9781107588783.022
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  • Useful Resources
  • Douglas Maraun, Karl-Franzens-Universität Graz, Austria, Martin Widmann, University of Birmingham
  • Book: Statistical Downscaling and Bias Correction for Climate Research
  • Online publication: 27 December 2017
  • Chapter DOI: https://doi.org/10.1017/9781107588783.022
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Useful Resources
  • Douglas Maraun, Karl-Franzens-Universität Graz, Austria, Martin Widmann, University of Birmingham
  • Book: Statistical Downscaling and Bias Correction for Climate Research
  • Online publication: 27 December 2017
  • Chapter DOI: https://doi.org/10.1017/9781107588783.022
Available formats
×