A package with tools for climate data geoprocessing.

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

The recommended installation procedure is to use the install_github command from the devtools R package (see the installation info in the wiki):

devtools::install_github(c("SantanderMetGroup/transformeR", "SantanderMetGroup/geoprocessoR"))

NOTE: Note that transformeR is a dependency for geoprocessoR. It also requires rgdal: install.packages("rgdal"). Note that transformeR also includes illustrative datasets for the climate4R framework.

EXAMPLE: The following code shows an example of climate4R data projection for gridded data (see the Wiki for more worked examples).

library(transformeR)
library(geoprocesoR)
data("EOBS_Iberia_pr")
plot(get2DmatCoordinates(EOBS_Iberia_pr))
grid <- projectGrid(EOBS_Iberia_pr,
                    original.CRS = "+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0",
                    new.CRS = "+init=epsg:28992")
plot(get2DmatCoordinates(grid))
# Use visualizeR to plot the mean climatology of the original and projected grids:
# devtools::install_github("SantanderMetGroup/visualizeR")
library(visualizeR)
spatialPlot(climatology(EOBS_Iberia_pr))
spatialPlot(climatology(grid))

References and further information:

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.

Cofiño et al. (2017) The ECOMS User Data Gateway: Towards seasonal forecast data provision and research reproducibility in the era of Climate Services. Climate Services, https://dx.doi.org/10.1016/j.cliser.2017.07.001.