CST_RFWeights {CSTools}

Compute climatological weights for RainFARM stochastic precipitation downscaling

Description

Compute climatological ("orographic") weights from a fine-scale precipitation climatology file.

Usage

CST_RFWeights(
  climfile,
  nf,
  lon,
  lat,
  varname = NULL,
  fsmooth = TRUE,
  lonname = "lon",
  latname = "lat",
  ncores = NULL
)

Arguments

climfile

Filename of a fine-scale precipitation climatology. The file is expected to be in NetCDF format and should contain at least one precipitation field. If several fields at different times are provided, a climatology is derived by time averaging. Suitable climatology files could be for example a fine-scale precipitation climatology from a high-resolution regional climate model (see e.g. Terzago et al. 2018), a local high-resolution gridded climatology from observations, or a reconstruction such as those which can be downloaded from the WORLDCLIM (https://www.worldclim.org) or CHELSA (https://chelsa-climate.org/) websites. The latter data will need to be converted to NetCDF format before being used (see for example the GDAL tools (https://gdal.org/). It could also be an 's2dv_cube' object.

nf

Refinement factor for downscaling (the output resolution is increased by this factor).

lon

Vector of longitudes.

lat

Vector of latitudes. The number of longitudes and latitudes is expected to be even and the same. If not the function will perform a subsetting to ensure this condition.

varname

Name of the variable to be read from climfile.

fsmooth

Logical to use smooth conservation (default) or large-scale box-average conservation.

lonname

A character string indicating the name of the longitudinal dimension set as 'lon' by default.

latname

A character string indicating the name of the latitudinal dimension set as 'lat' by default.

ncores

An integer that indicates the number of cores for parallel computations using multiApply function. The default value is one.

Value

An object of class 's2dv_cube' containing in matrix data the weights with dimensions (lon, lat).

Author(s)

Jost von Hardenberg - ISAC-CNR, [e-mail]

References

Terzago, S., Palazzi, E., & von Hardenberg, J. (2018). Stochastic downscaling of precipitation in complex orography: A simple method to reproduce a realistic fine-scale climatology. Natural Hazards and Earth System Sciences, 18(11), 2825-2840. doi: 10.5194/nhess-18-2825-2018.

Examples

# Create weights to be used with the CST_RainFARM() or RainFARM() functions
# using an external random data in the form of 's2dv_cube'.
obs <- rnorm(2 * 3 * 4 * 8 * 8)
dim(obs) <- c(dataset = 1, member = 2, sdate = 3, ftime = 4, lat = 8, lon = 8)
lon <- seq(10, 13.5, 0.5)
lat <- seq(40, 43.5, 0.5)
coords <- list(lon = lon, lat = lat)
data <- list(data = obs, coords = coords)
class(data) <- "s2dv_cube"
res <- CST_RFWeights(climfile = data, nf = 3, lon, lat, lonname = 'lon', 
                    latname = 'lat', fsmooth = TRUE)