meteoland

Functions to estimate weather variables at any position of a landscape.
https://github.com/emf-creaf/meteoland

Category: Natural Resources
Sub Category: Soil and Land

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Repository metadata

Functions to estimate weather variables at any position of a landscape

• Host: GitHub
• URL: https://github.com/emf-creaf/meteoland
• Owner: emf-creaf
• Created: 2019-06-06T10:43:19.000Z (almost 6 years ago)
• Default Branch: main
• Last Pushed: 2025-04-08T08:38:45.000Z (20 days ago)
• Last Synced: 2025-04-10T05:38:38.047Z (18 days ago)
• Language: R
• Homepage: https://emf-creaf.github.io/meteoland/
• Size: 54.6 MB
• Stars: 9
• Watchers: 2
• Forks: 3
• Open Issues: 8
• Releases: 7
• Metadata Files:
  • Readme: README.Rmd
  • Changelog: NEWS.md

README.Rmd

          ---
output: github_document
editor_options: 
  markdown: 
    wrap: 72
---

```{r setup, echo = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", 
                      fig.path = "man/figures/")
```

# Landscape meteorology tools 

[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/meteoland)](https://cran.r-project.org/package=meteoland)
[![](https://cranlogs.r-pkg.org/badges/meteoland)](https://cran.rstudio.com/web/packages/meteoland/index.html)
[![R-CMD-check](https://github.com/emf-creaf/meteoland/workflows/R-CMD-check/badge.svg)](https://github.com/emf-creaf/meteoland/actions)


## Important notice

Starting on June 2023, `rgdal`, `rgeos` and `maptools` R packages entered a *maintenance* mode (meaning no new updates). Coincidentally, `sp` and `raster` packages are now superseded by the more modern alternatives `sf`, `stars` and `terra`. This means that the `meteoland` classes, which are based on `sp`, need to be updated to deal with these changes in the R-spatial ecosystem.

Starting with version 2.0.0 of `meteoland` (February 2023) all functions, methods and classes based on or using the `sp`, `raster` and `rgdal` package were soft-deprecated. 

> **Since ver. 2.1.0, these functions, methods and classes have
> been removed from the package**

See the [*Tidy meteoland*](https://emf-creaf.github.io/meteoland/articles/tidy-meteoland.html) vignette for more info about this changes.

## 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 (De Cáceres et al 2018):

-   Spatial interpolation of daily weather records from meteorological stations.
-   Statistical correction of meteorological data series (e.g from climate models). Note that this functionality is deprecated starting in version 2.0.0. 
-   Multisite and multivariate stochastic weather generation. Note that this functionality is deprecated starting in version 2.0.0. 

## Package installation and documentation

Package **meteoland** can be found at [CRAN](https://cran.r-project.org/),
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):

```{r installation, eval = FALSE}
remotes::install_github("emf-creaf/meteoland")
```

Detailed documentation regarding **meteoland** calculation routines can be found at ().

## Companion packages

### Package meteospain

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**](https://emf-creaf.github.io/meteospain/), which can
also be found at
[CRAN](https://cran.rstudio.com/web/packages/meteospain/index.html).
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**](https://emf-creaf.github.io/meteospain/).

### Packages medfate and medfateland

Package **meteoland** has been designed to provide input weather data for simulations of forest function and dynamics via the following packages

  + Package [**medfate**](https://emf-creaf.github.io/medfate) provides functions for simulating forest function and dynamics.
  + Package [**medfateland**](https://emf-creaf.github.io/medfateland) extends **medfate** by allowing simulations to be performed in a spatially explicit context.

## Authorship

R package **meteoland** is developed and maintained by the [*Ecosystem
Modelling Facility*](https://emf.creaf.cat) 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.
    ().

        

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Owner metadata

• Name: Ecosystem Modelling Facility (CREAF)
• Login: emf-creaf
• Email: [email protected]
• Kind: organization
• Description:
• Website: https://emf.creaf.cat/
• Location:
• Twitter:
• Company:
• Icon url: https://avatars.githubusercontent.com/u/80052794?v=4
• Repositories: 10
• Last ynced at: 2023-03-08T06:13:46.862Z
• Profile URL: https://github.com/emf-creaf

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GitHub Events

Total

• Create event: 4
• Issues event: 5
• Release event: 2
• Delete event: 1
• Issue comment event: 3
• Push event: 17
• Pull request event: 3

Last Year

• Create event: 4
• Issues event: 5
• Release event: 2
• Delete event: 1
• Issue comment event: 3
• Push event: 17
• Pull request event: 3

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Committers metadata

Last synced: 8 days ago

Name	Email	Commits
Miquel De Cáceres	m****s@g****m	435
MalditoBarbudo	v****a@g****m	203
AntoineCabon	a****n@g****m	8
Steffi LaZerte	s****i@s****a	2
Miquel Decaceres	m****s@C****C	2
olivroy	5****y	1

Committer domains:

• steffi.ca: 1

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Issue and Pull Request metadata

Last synced: 1 day ago

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/emf-creaf/meteoland

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Package metadata

• Total packages: 1
• Total downloads:
  • cran: 528 last-month
• Total dependent packages: 1
• Total dependent repositories: 3
• Total versions: 25
• Total maintainers: 1

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Dependencies

Score: 10.903494237450115