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downscaleR: An R package for Statistical Downscaling

downscaleR is an R package for empirical-statistical downscaling focusing on daily data and covering the most popular approaches (bias correction, Model Output Statistics, Perfect Prognosis) and techniques. This package has been conceived to work in the framework of both seasonal forecasting and climate change studies. Thus, it considers ensemble members as a basic dimension of the data structure.

This package is part of the climate4R bundle, formed by loadeR, transformeR, downscaleR and visualizeR.

This wiki provides an up-to-date description of the package functionalities, with some worked examples:

0. Package Installation
1. Standard data manipulation
2. Bias Correction
   1. Simple application of the methods
      1. Working with a moving window (introducing seasonality)
   2. Application to Seasonal Forecasts
   3. How to contribute with new bias correction methods
3. Perfect Prognosis Approach
   1. Simple application of the analog method
   2. Simple application of linear regression
   3. Simple application of (Generalized) Linear Regression
   4. Conditioning to Weather Types
   5. Application to Seasonal Forecasts

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The development of this package has been partially supported by the SPECS project (Grant Agreement 308378) and was used in the SPECS hands-on training school on seasonal forecasting and downscaling held in Santander (Spain), from 10-12 September 2014 (see the wiki for an up-to-date version of the code used in the course).