Interpolation of normalized homogeneous data on a predefined grid
Homogenized data generated by homogen are normalized and
interpolated on a grid provided by the user at every time step, and saved in
a NetCDF file.
dahgrid(varcli, anyi, anyf, anyip=anyi, anyfp=anyf, grid, mh=FALSE, std=NA, ini=NA, obsonly=TRUE)
varcli |
Acronym of the name of the studied climatic variable, as in the data file name. |
anyi |
Initial year of the homogenized data. |
anyf |
Final year of the homogenized data. |
anyip |
First year of the desired reference period. (The reference period defaults to the whole period of the data). |
anyfp |
Last year of the desired reference period. |
grid |
Grid on which interpolations must be performed. |
mh |
If |
std |
Type of normalization:
|
ini |
Initial date (first of January of |
obsonly |
Do not interpolate estimated missing data. ( |
Homogenized data are read from the binary file ‘VAR_ANYI-ANYF.rda’
generated by homogen. Only series reconstructed from their
longest homogeneous sub-period are retained, and they are normalized by their
means (and standard deviations, if std=3), computed for the selected
reference period (or for the whole period of the data, by default).
Unless obsonly is set to FALSE, data that were missing in the
observed series are deleted to avoid interpolation of already interpolated
data.
Finally, the normalized homogeneous data are interpolated on the predefined grid for every time step using an inverse distance weight method, and the resulting grids are stored in a NetCDF file named ‘VAR_ANYIP-ANYFP.nc’, including grids of the reference means (and standard deviations, if applied).
The user must provide the grid as an object of class SpatialPixel, as in this example defining a grid from 40N,3E to 43N,7E with a resolution of 0.1 degrees:
grid <- expand.grid(x=seq(3,7,.1),y=seq(40,43,.1))library(sp)coordinates(grid) <- ~ x+y
The resolution of this grid need not be too high, but adjusted to the spatial density of the available series.
The user may be more interested in obtaining grids of absolute values, rather than normalized. This can be achieved simply by undoing the normalization on the grids with the help of the provided grids of reference means and standard deviations. However, the resulting grids will only be the product of a geometrical interpolation, and will not reflect the influence of orography and other physiographic effects on the studied climatic variable. Therefore, it is more advisable to derive better reference grids of means (and standard deviations, if needed) by applying a geostatistical model to the reference means (provided in the file ‘VAR_ANYIP-ANYFP_means.csv’ with their corresponding coordinates).
This better quality climatic maps will probably have a higher resolution than that of the grids of the NetCDF file provided by this function. In that case, these normalized grids must be interpolated to the grid of the geostatistically derived climatic maps before undoing the normalization to obtain the final maps of absolute values at all or selected time-steps of the studied period.
This function does not return any value, since the results are stored in files.
if(requireNamespace("ncdf4") & requireNamespace("sp")) {
#Set a temporal working directory and write input files:
wd <- tempdir()
wd0 <- setwd(wd)
data(pcp_results) #Load homogenization data
#Write homogenization results as expected by the dahgrid function:
save(dah,dat,est.c,ini,nd,ndec,ne,nei,nm,std,x,file='pcp_2001-2010.rda')
#Now run the example:
grd <- expand.grid(x=seq(2.9,3.3,.1),y=seq(39.5,39.7,.1))
sp::coordinates(grd) <- ~ x+y
dahgrid('pcp',2001,2010,grid=grd)
#Return to user's working directory:
setwd(wd0)
#Input and output files can be found in directory:
print(wd)
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