> float lat(rlat, rlon) ;
> lat:standard_name = "latitude" ;
> lat:long_name = "latitude" ;
> lat:units = "degrees_north" ;
> float lon(rlat, rlon) ;
> lon:standard_name = "longitude" ;
> lon:long_name = "longitude" ;
> lon:units = "degrees_east" ;
These two previous variables are grids of lat-lon values that
correspond to the data. I nabbed a .ncdf file of runoff values from a
site:
library(ncdf)
r = open.ncdf("runoff.SMHI.MPIB2.nc")
dim(get.var.ncdf(r,"lat"))
[1] 90 86
dim(get.var.ncdf(r,"lon"))
[1] 90 86
so when you get the 90x86 grids of runoff values, the lat-long
coordinates are the corresponding ones in the "lat" and "lon"
variables.
lon = get.var.ncdf(r,"lon")
lat = get.var.ncdf(r,"lat")
# get data for t = 1:
t1 = get.var.ncdf(r,"runoff",start=c(1,1,1),count=c(90,86,1))
now you have three 90x86 matrices. To get (x,y,runoff) matrix, we
just put them together, something like:
library(sp)
xyr = data.frame(cbind(as.vector(lat),as.vector(lon),as.vector(t1)))
names(xyr)=c("lat","lon","runoff")
coordinates(xyr)=~lon+lat
spplot(xyr,"runoff")
- shows how non-griddy the projection really is - it should look like
a fan since now we are showing the data on true lat-long points, not
the transformed rlat rlong points.
The rlat and rlon variables are the grid coordinate system, which
appears to be some non-standard conical projection. Climate
scientists!
Details: http://prudence.dmi.dk/public/DDC/areas.html
Barry
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