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https://github.com/SantanderMetGroup/loadeR

A climate4R package for data access
https://github.com/SantanderMetGroup/loadeR

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A climate4R package for data access

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README 

        
# What is loadeR?



loadeR is an R package for climate data access building on the NetCDF-Java API. It allows user-friendly data access either from local or remote locations (e.g. OPeNDAP servers) and it is fully integrated with the User Data Gateway ([UDG](http://www.meteo.unican.es/udg-wiki)), a Climate Data Service deployed and maintained by the [Santander Meteorology Group](http://www.meteo.unican.es). loadeR 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 its two main data structures (`grid` and `station`). Find out more about this package at the [loadeR wiki](https://github.com/SantanderMetGroup/loadeR/wiki).



This package is part of the [climate4R framework](http://www.meteo.unican.es/climate4r), formed by `loadeR`, `transformeR`, `downscaleR`, `visualizeR` and other packages dealing with climate data analysis and visualization.



The recommended installation procedure (for loader and the companion loadeR.java and climate4R.UDG packages) is to use the `install_github` command from the devtools R package (see the installation info in the wiki):



```r

devtools::install_github(c("SantanderMetGroup/loadeR.java", "SantanderMetGroup/climate4R.UDG", "SantanderMetGroup/loadeR"))

```

**IMPORTANT:** On OS X, be sure to execute this in R started from the Terminal, not the R App! (This is because the R app doesn’t honor $PATH changes in ~/.bash_profile)



**IMPORTANT:** The package requires Java version 1.7 or higher. Several _recommendations for known problems with R and Java_ are given in the [wiki installation info](https://github.com/SantanderMetGroup/loadeR/wiki/Installation)). 

 

**NOTE:** loadeR is enhanced by [loadeR.ECOMS](http://meteo.unican.es/udg-wiki/ecoms/RPackage) package which allows to remotely access harmonized data from several state-of-the-art seasonal forecasting databases stored at the ECOMS-UDG. 



---

Reference and further information: 



**[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](https://github.com/SantanderMetGroup/notebooks) with worked examples.



**[Statistical Downscaling]** Bedia et al. (2020). Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment. *Geoscientific Model Development*, 13, 1711–1735. https://doi.org/10.5194/gmd-13-1711-2020



**[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



**[Example of a sectoral application (fire danger)]** Bedia et al. (2018) Seasonal predictions of Fire Weather Index: Paving the way for their operational applicability in Mediterranean Europe. *Climate Services*, 9, 101-110. http://doi.org/10.1016/j.cliser.2017.04.001