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
andvisualizeR
. These packages are fully documented in the corresponding GitHub wikis. -
climate4R
capabilities are further extended by providing support to physical units handling (convertR
package) and geoprocessing tasks (geoprocessoR
package). -
Compatibility with some external packages has been achieved by wrapping packages, thus enhancing
climate4R
with new functionalities (e.g. ETCCDI extreme climate indices via theclimdex
package). -
Semantic provenance (metadata) information for
climate4R
products can be generated using METACLIP via themetaclipR
package. -
Conda and docker
climate4R
installations available. The docker file also includes the jupyter framework. This is the base layer for the climate4R Hub (a cloud-based computing facility to runclimate4R
notebooks at IFCA/CSIC Cloud Services).
The formal reference of climate4R
is:
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
Additional references for specific components of climate4R
(with worked examples) are Cofiño et al. 2018 (seasonal forecasting ), Frías et al. 2018 (visualization), Bedia et al. 2019 (data provenance) and Bedia et al. 2019a (statistical downscaling). Other publications describing applications in sectoral impact studies (also with worked out examples) are Bedia et al. (2018) (fire danger) or Iturbide et al. (2018) (Species distribution models), among others.
Moreover, there is a notebook repository including several illustrative notebooks with worked-out examples (which are companion material of several papers).
The climate4R
framework relies on a wealth of other R packages and bindings to third-party libraries. Therefore, the recommended installation is through the conda package below, which ensures the proper installation of all dependencies.
Miniconda is a free minimal installer for conda. The conda recipe installs an up-to-date version of the different packages composing the climate4R
framework, along with the associated library dependencies (udunits, openjdk, netcdf Java etc.), avoiding potential problems like the R-java configuration etc. Note that the appropriate miniconda distribution must be installed (go to the miniconda installers page) before running the following commands. We recommend starting from a clean environment (named climate4R in this example):
conda create --name climate4R
conda activate climate4R
In this environment, install climate4R
by issuing:
mamba install -y -c conda-forge -c r -c defaults -c santandermetgroup r-climate4r
Activate the conda environment to work with climate4R. Deactivate the environment with:
conda deactivate
Individual packages can be installed directly from the github sources.
> library(devtools)
> install_github(c("SantanderMetGroup/loadeR.java",
"SantanderMetGroup/climate4R.UDG",
"SantanderMetGroup/loadeR",
"SantanderMetGroup/transformeR",
"SantanderMetGroup/visualizeR",
"SantanderMetGroup/downscaleR"))
In case a particular paper notebook is to be replicated, the installation of specific version tags can be done by just explicitly indicating the tag number in the repo name. For example:
> devtools::install_github("SantanderMetGroup/visualizeR@v1.0.0")
installs the visualizeR
package version used in Frías et al. 2018, while the following
> devtools::install_github("SantanderMetGroup/visualizeR@v1.4.6")
will install a more recent version of the package used in the paper by Iturbide et al. 2019.
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.
Please note that the pool of people who can provide support for climate4R
packages is very small and our time for support is limited. We don't necessarily have the capacity for long, open-ended user support. Please follow these basic guidelines before posting:
- Introduce the problem before you post any code
- Help others reproduce the problem
- Avoid sending the same question to multiple places
These posting guidelines at stackoverflow provide further recommendations on how to make a good question. If questions are kept short, specific and direct, there's a greater chance that someone will take on the ticket.