Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.154 IF 5.154
  • IF 5-year value: 5.697 IF 5-year
    5.697
  • CiteScore value: 5.56 CiteScore
    5.56
  • SNIP value: 1.761 SNIP 1.761
  • IPP value: 5.30 IPP 5.30
  • SJR value: 3.164 SJR 3.164
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 59 Scimago H
    index 59
  • h5-index value: 49 h5-index 49
Discussion papers
https://doi.org/10.5194/gmd-2019-224
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-224
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 16 Sep 2019

Submitted as: development and technical paper | 16 Sep 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Statistical downscaling with the downscaleR package: Contribution to the VALUE intercomparison experiment

Joaquín Bedia1, Jorge Baño-Medina2, Mikel N. Legasa2, Maialen Iturbide2, Rodrigo Manzanas1, Sixto Herrera1, Ana Casanueva1, Daniel San-Martín3, Antonio S. Cofiño1, and Jose Manuel Gutiérrez2 Joaquín Bedia et al.
  • 1Meteorology Group. Dpto. de Matemática Aplicada y Ciencias de la Computación, Universidad de Cantabria, Santander, 39005, Spain
  • 2Meteorology Group. Instituto de Física de Cantabria (CSIC - Universidad de Cantabria), Santander, 39005, Spain
  • 3Predictia Intelligent Data Solutions, Santander, 39005, Spain

Abstract. The increasing demand for high-resolution climate information has attracted a growing attention for statistical downscaling methods (SD), due in part to their relative advantages and merits as compared to dynamical approaches (based on regional climate model simulations), such as their much lower computational cost and their fitness-for-purpose for many local-scale applications. As a result, a plethora of SD methods is nowadays available for climate scientists, which has motivated recent efforts for their comprehensive evaluation, like the VALUE Project (http://www.value-cost.eu). The systematic intercomparison of a large number of SD techniques undertaken in VALUE, many of them independently developed by different authors and modeling centers in a variety of languages/environments, has shown a compelling need for new tools allowing for their application within an integrated framework. With this regard, downscaleR is an R package for statistical downscaling of climate information which covers the most popular approaches (Model Output Statistics – including the so called 'bias correction' methods – and Perfect Prognosis) and state-of-the-art techniques. It has been conceived to work primarily with daily data and can be used in the framework of both seasonal forecasting and climate change studies. Its full integration within the climate4R framework (Iturbide et al. 2019) makes possible the development of end-to-end downscaling applications, from data retrieval to model building, validation and prediction, bringing to climate scientists and practitioners a unique comprehensive framework for SD model development.

In this article the main features of downscaleR are showcased through the replication of some of the results obtained in the VALUE Project, making an emphasis in the most technically complex stages of perfect-prog model calibration (predictor screening, cross-validation and model selection) that are accomplished through simple commands allowing for extremely flexible model tuning, tailored to the needs of users requiring an easy interface for different levels of experimental complexity. As part of the open-source climate4R framework, downscaleR is freely available and the necessary data and R scripts to fully replicate the experiments included in this paper are also provided as a companion notebook.

Joaquín Bedia et al.
Interactive discussion
Status: open (until 14 Nov 2019)
Status: open (until 14 Nov 2019)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Joaquín Bedia et al.
Model code and software

downscaleR J. Bedia, A. Casanueva, A. S. Cofiño, J. Fernández, M. D. Frías, J. M. Gutiérrez, S. Herrera, Ma. Iturbide, M. N. Legasa, R. Manzanas, J. B. Medina, D. San Martín, and M. Tuni https://doi.org/10.5281/zenodo.3277316

Joaquín Bedia et al.
Viewed  
Total article views: 240 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
179 59 2 240 2 2
  • HTML: 179
  • PDF: 59
  • XML: 2
  • Total: 240
  • BibTeX: 2
  • EndNote: 2
Views and downloads (calculated since 16 Sep 2019)
Cumulative views and downloads (calculated since 16 Sep 2019)
Viewed (geographical distribution)  
Total article views: 185 (including HTML, PDF, and XML) Thereof 184 with geography defined and 1 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 15 Oct 2019
Publications Copernicus
Download
Short summary
We introduce downscaleR, an open-source tool for statistical downscaling (SD) of climate information, implementing the most popular approaches and state-of-the-art techniques. It makes possible the development of end-to-end downscaling applications, from data retrieval to model building, validation and prediction, bringing to climate scientists and practitioners a unique comprehensive framework for the development of complex and fully reproducible SD experiments.
We introduce downscaleR, an open-source tool for statistical downscaling (SD) of climate...
Citation