R Framework for Climate Data Access and Post-processing

climate4R is a bundle of R packages for transparent climate data access, post-processing (including data collocation and bias correction / downscaling) and visualization. climate4R builds on two main data structures (grid and station, including metadata) to deal with gridded and point data from observations, reanalysis, seasonal forecasts and climate projections. It considers ensemble members as a basic dimension of the data structures. Moreover, climate4R is transparently (and remotely) connected to the Santander Climate Data Gateway, offering several state-of-the-art datasets (including CMIP5 and CORDEX subsets).

• climate4R is formed by the following four core packages (all in GitHub): loadeR , transformeR, downscaleR and visualizeR. These packages are fully documented in the corresponding GitHub wikis.

• Other useful packages also forming the climate4R bundle are geoprocessoR and convertR.

• Compatibility with some external packages has been achieved by wrapping packages, thus enhancing climate4R with new functionalities (e.g. ETCCDI extreme climate indices via the climdex package).

• Semantic provenance (metadata) information for climate4R products can be generated using METACLIP via the metaclipR package.

• A docker file with pre-installed climate4R and jupyter frameworks is in preparation. This is the base layer for the climate4R Hub (a cloud-based computing facility to run climate4R notebooks at IFCA/CSIC Cloud Services).

References and Examples

The formal reference of climate4R is:

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M. Iturbide, J. Bedia, S. Herrera, J. Baño-Medina, J. Fernández, M.D. Frías, R. Manzanas, D. San-Martín, E. Cimadevilla, A.S. Cofiño and JM Gutiérrez (2019) The R-based climate4R open framework for reproducible climate data access and post-processing. Environmental Modelling & Software, 111, 42-54. DOI: /10.1016/j.envsoft.2018.09.009

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Additional references for specific components of climate4R (with worked out examples) are Cofiño et al. 2018 (seasonal forecasting ) and Frías et al. 2018 (visualization). Other publications describing applications in sectoral impact studies (also with worked out examples) are Bedia et al. (2018) (fire danger), and Iturbide et al. (2018) (Species distribution models).

Moreover, there is a notebook repository including several illustrative notebooks with worked-out examples (which are companion material of several papers).

Installation

    > library(devtools)
    > install_github(c("SantanderMetGroup/loadeR.java",
                 "SantanderMetGroup/loadeR",
                 "SantanderMetGroup/transformeR",
                 "SantanderMetGroup/visualizeR",
                 "SantanderMetGroup/downscaleR"))

Example of use

Examples of use of the climate4R framework are given in the reference papers above. In the following we illustrate the main functionalities of climate4R with a simple example, consisting on calculating an ETCCDI index (Summer Days) from bias corrected EURO-CORDEX data over Southern Europe. More details at the brief introduction to climate4R document in the man folder and full code at the companion jupyter notebook.