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Joaquin Bedia edited this page Jun 9, 2020 · 8 revisions

downscaleR builds on the data structures of the climate4R bundle (point and grid data). The manipulation and transformation functions for these data structures are packed in transformeR, which allows to easily perform typical climate data postprocessing in a flexible way, including:

  • Spatial and temporal subsetting and aggregation
  • Regridding and interpolation
  • Detrending and application of different time filters
  • EOF/PCA analysis
  • Clustering and weather typing (soon available).
  • etc.

It also contains several (data(package = "climate4R.datasets"), note that the package climate4R.datasets must be installed) and provides parallel computing support and different internal utilities for the R package downscaleR. For details and worked examples on the use of transformeR go to the corresponding wiki.

NOTES:

  1. The utilities in transformeR were formerly part of downscaleR (up to v1.3-4). Since downscaleR v2.0-0, these are in transformeR and downscaleR is strictly aimed to statistical downscaling and bias correction.
  2. The example datasets were part of transformeR in former versions, but are now included in a dedicated package: climate4R.datasets

<-- Home page of the Wiki

downscaleR - Santander MetGroup (Univ. Cantabria - CSIC)

  1. Package Installation
  2. Data manipulation with transformeR
  3. Bias Correction
    1. Calibration and cross validation
      1. Working with a moving window
    2. Bias correction of climate change projections
    3. Bias correction of seasonal forecasts
    4. How to contribute with new bias correction methods
  4. Downscaling: Perfect Prognosis Approach
    1. Preparing predictor data
    2. Training and cross-validation
    3. Downscaling climate change projections
    4. Downscaling seasonal forecasts
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