Landscape Meteorology Tools

meteoland package provides functions to estimate daily weather variables (temperature, realtive humidity, precipitation...) over landscapes, by means of interpolation and statistical corrections.

Here you will learn how to use the meteoland R package to interpolate weather from meteorological stations data, and to downscale/correct predictions resulting from regional climate models

In addition, an interactive shiny app is provided to illustrate the package capabilities, using the Catalan region as an example.

Package installation

Package meteoland can be found at CRAN, where new versions are updated every 3-4 months. Users can download and install the latest stable versions GitHub as follows:


Required packages devtools and curl may be installed/updated first. Documentation on the models included in meteoland and how to run them using the package functions can be found below. Additionally, users can have help to run package functions directly as package vignettes, by forcing their inclusion in installation:

devtools::install_github("miquelcaceres/meteoland", build_vignettes=TRUE)

Package vignettes

Tutorials explaining the main functions of the package

Simple guide to start using the package

Detailed description of package functions and mathematical calculations of each procedure

A short guide to learn to use the shiny app included in this web page


Please, cite the package as follows:

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.

Grid mode selected.

Please provide the upper left coordinates and the bottom right coordinates of the desired grid.

Coordinates input must be in latitude/logitude Mercator projection, in decimal format

Selected points:


Relative Humidity

Precipitation & PET

Grid Plot

Grid Plot

Grid Plot


Selected point topographic info:

These are the cross-validation results for the process of interpolating daily meteorological data. Validation was done by making predictions for the location of each metereological station after excluding its data from the model. Cross-validation was conducted for each year in the 1976-2016 period separately.

Year Statistics Table:

Statistic Temporal Variation:

Stations Bubble Plot by Statistic and Variable


This web-based service has been develop by the Vegetation Modeling Group at Forest Sciences Centre of Catalonia (CTFC), in collaboration with the Center for Ecological Research and Forestry Applications (CREAF):

  • Idea and conceptual design: Miquel de Cáceres (CTFC/CREAF), Víctor Granda (CTFC) & Antoine Cabon (CTFC/CREAF).

  • Package development: Miquel De Cáceres (CTFC/CREAF), Antoine Cabon (CTFC/CREAF), Nicolas Martin StPaul (INRA) & Víctor Granda (CTFC).

  • User interface: Víctor Granda (CTFC).

  • Contact: Miquel De Cáceres (

Data sources

Funding projects

  • Projects: INFORMED (PCIN-2014-050), FORESTCAST (CGL2014-59742-C2-2-R, Ministerio de Economía y Competitividad), DRESS (CGL2017-89149-C2-2-R, Ministerio de Economía y Competitividad).

  • Fellowships: RYC-2012-11109 to M. De Cáceres (Ministerio de Economía y Ciencia).


The data offered in this website is the result of modelling exercises and the accuracy of results may be impaired by several factors, like inaccuracies of input data, inappropriate model design or parameterization. Users should be aware of all these limitations when using the data offered here. We decline all responsibility for how the data in this website is used by third parties.