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downscaleR

An R package for climate data bias correction and downscaling (part of the climate4R bundle)

https://github.com/SantanderMetGroup/downscaleR

Science Score: 49.0%

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    Found 9 DOI reference(s) in README
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Keywords from Contributors

cordex climate-change-atlas climate4r cmip6 ipcc-regions warming-levels
Last synced: 7 months ago · JSON representation

Repository

An R package for climate data bias correction and downscaling (part of the climate4R bundle)

Basic Info
Statistics
  • Stars: 107
  • Watchers: 26
  • Forks: 61
  • Open Issues: 37
  • Releases: 45
Created over 12 years ago · Last pushed about 1 year ago
Metadata Files
Readme Changelog License

README.md

DOI

What is downscaleR?

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 (e.g. quantile mapping, regression, analogs, neural networks). 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. Find out more about this package at the downscaleR wiki.

This package is part of the climate4R bundle, formed by loadeR, transformeR, downscaleR and visualizeR. The recommended installation procedure is to use the install_github command from the devtools R package:

r devtools::install_github(c("SantanderMetGroup/transformeR", "SantanderMetGroup/downscaleR")) NOTE: Note that transformeR is a dependency for downscaleR. 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. Note that transformeR also includes illustrative datasets for the climate4rframework.

EXAMPLE: The following code trains three different downscaling methods (analogs, linear regression and neural networks) using principal components (explaining 95\% of the variance for each variable) and visualizes the results (the illustrative station and reanalysis data for DJF included in the transformeR package is used in this example): ```r library(downscaleR) data("VALUEIberiatas") # illustrative datasets included in transformeR y <- VALUEIberiatas data("NCEPIberiahus850", "NCEPIberiapsl", "NCEPIberiata850") x <- makeMultiGrid(NCEPIberiahus850, NCEPIberiapsl, NCEPIberiata850)

calculating predictors

data <- prepareData(x = x, y = y,spatial.predictors = list(v.exp = 0.95))

Fitting statistical downscaling methods (simple case, no cross-validation)

analog <- downscale.train(data, method = "analogs", n.analogs = 1) regression <- downscale.train(data, method = "GLM",family = gaussian) neuralnet <- downscale.train(data, method = "NN", hidden = c(10,5), output = "linear")

Extracting the results for a particula station (Igueldo) for a single year (2000)

igueldo.2000 <- subsetGrid(y,station.id = "000234",years = 2000) analog.2000 <- subsetGrid(analog$pred,station.id = "000234",years = 2000) regression.2000 <- subsetGrid(regression$pred,station.id = "000234",years = 2000) neuralnet.2000 <- subsetGrid(neuralnet$pred,station.id = "000234",years = 2000) library(visualizeR) # Data visualization utilities temporalPlot(igueldo.2000, analog.2000, regression.2000, neuralnet.2000) ```

Reference and further information:

[General description of the downscaleR package] Bedia et al. (2020) Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment. Geosientific Model Development, 13, 1711–1735, https://doi.org/10.5194/gmd-13-1711-2020 Check out the companion notebooks GitHub.

[General description of the climate4R framework] Iturbide et al. (2019) The R-based climate4R open framework for reproducible climate data access and post-processing. Environmental Modelling and Software, 111, 42-54. https://doi.org/10.1016/j.envsoft.2018.09.009 Check out the companion notebooks for the two examples GitHub.

[Seasonal forecasting applications] Cofiño et al. (2018) The ECOMS User Data Gateway: Towards seasonal forecast data provision and research reproducibility in the era of Climate Services. Climate Services, 9, 33-43. http://doi.org/10.1016/j.cliser.2017.07.001

Owner

  • Name: Santander Meteorology Group (UC-CSIC)
  • Login: SantanderMetGroup
  • Kind: organization
  • Location: Santander

a multidisciplinary approach to weather & climate

GitHub Events

Total
  • Watch event: 2
  • Issue comment event: 1
  • Push event: 2
  • Fork event: 1
Last Year
  • Watch event: 2
  • Issue comment event: 1
  • Push event: 2
  • Fork event: 1

Committers

Last synced: 8 months ago

All Time
  • Total Commits: 1,009
  • Total Committers: 13
  • Avg Commits per committer: 77.615
  • Development Distribution Score (DDS): 0.396
Past Year
  • Commits: 2
  • Committers: 1
  • Avg Commits per committer: 2.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
jbedia j****a@g****m 609
miturbide m****e@g****m 195
Jorge b****a@i****s 50
Sixto Herrera García h****s@u****s 42
szabotakacsb b****s@g****u 28
Jose M. Gutierrez g****m@u****s 26
Ana a****2@g****m 21
sixtohg s****g@g****m 15
Jorge Bano Medina j****a@M****l 10
jorgebanomedina j****a@g****m 7
Max Tuni m****a@g****m 4
jesusff j****f@g****m 1
Matte De Felice m****e@g****m 1
Committer Domains (Top 20 + Academic)
unican.es: 2 gmail.hu: 1 ifca.unican.es: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time
  • Total issues: 92
  • Total pull requests: 9
  • Average time to close issues: 5 months
  • Average time to close pull requests: 14 days
  • Total issue authors: 50
  • Total pull request authors: 7
  • Average comments per issue: 1.5
  • Average comments per pull request: 1.44
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
Issue Authors
  • matteodefelice (15)
  • jbedia (10)
  • idriswada007 (3)
  • Natel-Carolina (3)
  • cyndyfem (3)
  • durutti (3)
  • anubhavchoudhary (3)
  • tubabucak (2)
  • arulalant (2)
  • miturbide (2)
  • scbrown86 (2)
  • ABY1950 (2)
  • sdbht (2)
  • Freestyleyang (2)
  • Violet-issak (2)
Pull Request Authors
  • matteodefelice (2)
  • cofinoa (2)
  • szabotakacsb (2)
  • jorgebanomedina (1)
  • jbedia (1)
  • dlebauer (1)
  • gutierjm (1)
Top Labels
Issue Labels
enhancement (11) bug (9) question (4) duplicate (1) wontfix (1)
Pull Request Labels

Packages

  • Total packages: 3
  • Total downloads: unknown
  • Total dependent packages: 1
    (may contain duplicates)
  • Total dependent repositories: 2
    (may contain duplicates)
  • Total versions: 50
proxy.golang.org: github.com/santandermetgroup/downscaler
  • Versions: 24
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 7.0%
Average: 8.2%
Dependent repos count: 9.3%
Last synced: 7 months ago
proxy.golang.org: github.com/SantanderMetGroup/downscaleR
  • Versions: 24
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 7.0%
Average: 8.2%
Dependent repos count: 9.3%
Last synced: 7 months ago
conda-forge.org: r-downscaler
  • Versions: 2
  • Dependent Packages: 1
  • Dependent Repositories: 2
Rankings
Dependent repos count: 20.1%
Forks count: 23.6%
Average: 26.8%
Dependent packages count: 29.0%
Stargazers count: 34.4%
Last synced: 7 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.5.0 depends
  • transformeR >= 2.0.1 depends
  • MASS * imports
  • RCurl * imports
  • abind * imports
  • deepnet * imports
  • evd * imports
  • fields * imports
  • glmnet * imports
  • magrittr * imports
  • parallel * imports
  • reticulate * imports
  • stats * imports
  • sticky * imports
  • utils * imports
  • climate4R.datasets * suggests
  • knitr * suggests
  • loadeR * suggests
  • loadeR.ECOMS * suggests
  • rmarkdown * suggests
  • visualizeR * suggests