Introduction

With the aim to assist research of climatic impacts on forests, the R package meteoland provides utilities to estimate daily weather variables at any position over complex terrains:

  • Spatial interpolation of daily weather records from meteorological stations.
  • Statistical correction of meteorological data series (e.g. from climate models).
  • Multisite and multivariate stochastic weather generation.

A more detailed introduction to the package functionality can be found in De Cáceres et al. (2018).

Package installation and documentation

Package meteoland can be found at CRAN, but the version in this repository may not be the most recent one. Latest stable versions can be downloaded and installed from GitHub as follows (package remotes should be installed first):

remotes::install_github("emf-creaf/meteoland")

Alternatively, users can have help to run package functions directly as package vignettes, by forcing their inclusion in installation:

remotes::install_github("emf-creaf/meteoland", 
                        build_opts = c("--no-resave-data", "--no-manual"),
                        build_vignettes = TRUE)

Detailed documentation on meteoland calculation routines can be found at (https://emf-creaf.github.io/meteolandbook/index.html).

Companion packages

During the development of meteoland some functions to download weather station data from several Spanish networks were originally developed. After meteoland version 1.0.1, the user is recommended to use package meteospain, which can also be found at CRAN. Functions to download weather station data are still available in meteoland but they have been deprecated and make internal calls to functions in package meteospain.

The two R packages are developed and maintained by the Ecosystem Modelling Facility at CREAF (Catalonia, Spain).

References

  • De Caceres M, Martin-StPaul N, Turco M, Cabon A, Granda V (2018) Estimating daily meteorological data and downscaling climate models over landscapes. Environmental Modelling and Software 108: 186-196. (doi:10.1016/j.envsoft.2018.08.003).