rfweights: Derive weights from a fine-scale precipitation climatology
In rainfarmr: Stochastic Precipitation Downscaling with the RainFARM Method

Description Usage Arguments Value Author(s) References Examples

Weights for downscaling are computed interpolating a fine-scale climatology to the target grid and dividing it by an averaged version of itself. A suitable climatology could be represented for example by a fine-scale precipitation climatology from a high-resolution regional climate model (see e.g. Terzago et al. (2018) doi: 10.5194/nhess-18-2825-2018), a local high-resolution gridded climatology from observations, or a reconstruction such as those which can be downloaded from the WORLDCLIM or CHELSA websites. The latter data could be converted to NetCDF format using for example the GDAL tools.

1	rfweights(z, lon, lat, lonc, latc, nf, fsmooth = TRUE)

`z`	matrix with a spatial field of fine-scale precipitation climatology.
`lon`	vector of longitudes of the high-resolution climatology
`lat`	vector of latitudes of the high-resolution climatology
`lonc`	vector of longitudes of the coarse field to downscale
`latc`	vector of latitudes of the coarse field to downscale
`nf`	refinement factor for downscaling (the coarse resolution is increased by this factor). The number of longitudes and latitudes is expected to be equal.
`fsmooth`	logical to compute weights against a smooth average. If false box averaging is used.

The matrix of weights with dimensions c(lonc*nf, latc*nf)

Jost von Hardenberg, j.vonhardenberg@isac.cnr.it

Terzago, S. et al. (2018). NHESS 18(11), 2825<e2><80><93>2840 doi: 10.5194/nhess-18-2825-2018.

# Make synthetic fine-scale precipitation climatology
z <- exp(metagauss(initmetagauss(1.7, 64)))
# Specify lon and lat of the input
lon <- seq(10,17.875,0.125)
lat <- seq(40,47.875,0.125)
# Specify lon and lat of the coarse field and the downscaling factor
lonc <- seq(12,15.5,0.5)
latc <- seq(42,45.5,0.5)
nf <- 4
ww <- rfweights(z, lon, lat, lonc, latc, nf)