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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), a Climate Data Service deployed and maintained by the Santander Meteorology Group. 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.

This package is part of the climate4R framework, 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 and climate4R.UDG packages) is to use the install_github command from the devtools R package (see the installation info in the wiki):

devtools::install_github(c("SantanderMetGroup/", "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).

NOTE: loadeR is enhanced by loadeR.ECOMS 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. Check out the companion 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.

[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.

[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.