Using 'disaggregate' with GCM data - r

Question

I have data from various Global Circulation Models (GCM) that I need in at a finer resolution to perturb climate observations that are 0.5 degree pixel. I saw that I could use disaggregate because this function won't change pixels values, as 'resample' does using, e.g., the bilinear method. But still, the output doesn't match my fine-res-grids.
Here an example with the dimensions of the files I'm dealing with:
r = raster(ncols=720, nrows=360) #fine resolution grid
r[] = runif(1:100)
> r
class : RasterLayer
dimensions : 360, 720, 259200 (nrow, ncol, ncell)
resolution : 0.5, 0.5 (x, y)
extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
data source : in memory
names : layer
values : 0.0159161, 0.9876637 (min, max)
s = raster(ncols=192, nrows=145) #dimensions of one of the GCM
s[] = runif(1:10)
> s
class : RasterLayer
dimensions : 145, 192, 27840 (nrow, ncol, ncell)
resolution : 1.875, 1.241379 (x, y)
extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
data source : in memory
names : layer
values : 0.03861309, 0.9744665 (min, max)
d=disaggregate(s, fact=c(3.75,2.482759)) #fact equals r/s for cols and rows
> d
class : RasterLayer
dimensions : 290, 768, 222720 (nrow, ncol, ncell)
resolution : 0.46875, 0.6206897 (x, y)
extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
data source : in memory
names : layer
values : 0.03861309, 0.9744665 (min, max)
The dimensions of 'd' are not equal to the dimensions of 'r', so I can't do operations with the 2 grids. And I'm not meant to be interpolating the pixel values. So, what's the best method to achieve the disaggregation with GCM data?
Thanks in advance.

Answer 1

The code below should help- it uses aggregate to the closest integer scaling possible then resample to match the other raster's spatial characteristics exactly:
r = raster(ncols=720, nrows=360) #fine resolution grid
r[] = runif(1:100)
s = raster(ncols=192, nrows=145) #dimensions of one of the GCM
s[] = runif(1:10)
d=disaggregate(s, fact=c(round(dim(r)[1]/dim(s)[1]),round(dim(r)[2]/dim(s)[2])), method='') #fact equals r/s for cols and rows
e=resample(d, r, method="ngb")
But there a few caveats/ warnings: If you want to have the same values as the original raster, use disaggregate with method='' or else it will interpolate. But most important looking at the aspect ratio between your r and s rasters, they are not the same: dim(r)[1]/dim(s)[1] != dim(r)[2]/dim(s)[2]). I would double check the original data because if there is a difference in resolution, projection, or extent you will not get what you want from the steps above.