Reliable meteorological data are a basic requirement for hydrological and ecological studies at the landscape scale. Given the large spatial variation of meteorology over complex terrains, meteorological records from a single weather station are often not representative of entire landscapes. Studies made on multiple sites over a landscape require different meteorological series for each site; and other studies may require meteorological data series for all grid cells of a landscape, in a continuous way. In these cases, spatial correlation between the meteorology series of different sites or cells must be taken into account. For example, the sequence of days with rain of contiguous cells will normally be the same or very similar, even if precipitation amounts may differ. Finally, studies addressing the impacts of climate change on forests and landscapes require downscaling coarse-scale predictions of global or regional climate models to the landscape scale. When downscaling predictions for several locations in a landscape, spatial correlation of predictions is also important.

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:

1. Spatial interpolation of daily weather records from meteorological stations.

2. Statistical correction of meteorological data series (e.g. from climate models).

Spatial interpolation is required when meteorology for the area and period of interest cannot be obtained from local sensors. The nearest weather station may not have data for the period of interest or it may be located too far away to be representative of the target area. Correcting the biases of a meteorological data series containing biases using a more accurate meteorological series is necessary when the more accurate series does not cover the period of interest and the less accurate series does. The less accurate series may be at coarser scale, as with climate model predictions or climate reanalysis data. In this case one can speak of statistical correction adn downscaling. However, one may also correct the predictions of climate models using reanalysis data estimated at the same spatial resolution.

Link to paper:

https://www.sciencedirect.com/science/article/pii/S1364815217309830Link to R Manual:

https://cran.r-project.org/web/packages/meteoland/meteoland.pdfLink to User Guide:

https://cran.r-project.org/web/packages/meteoland/vignettes/Meteorology.pdf
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