On Mon, 12 Jul 2010, Paul Hiemstra wrote:
Hi Matt,
The crux here is to find the pro4 string associated with your
projection. After finding that, you can use spTransform to reproject
the data. However, after reprojection the T62 grid is not a grid any
more. You already observed this, your pixelsize changes with
latitude. This means that you have to perform some kind of
interpolation, warping or resampling.
One option to find the proj4 string is to post a question on the
proj4 list [1]. The response on this is often quite good and fast.
The key thing is that these grids are not really grids, as the step in
the y (latitude) direction varies with latitude. Their planar
representation, see
http://www.cccma.ec.gc.ca/data/grids/geom_llg_96x48.shtml for example,
is also very misleading near the poles. They are point data in
geographical coordinates, or very possibly polygon support data with
near-trapezoidal form (the north and south borders are straight lines,
the east and west borders curves), centred on the identifying point.
They are a "fudge" to get something like equal area "rectangular"
cells on a sphere (typically displayed as squares). An alternative are
icosahedral?hexagonal grids or spherical geodesic grids, see
http://kiwi.atmos.colostate.edu/pubs/CISE.pdf. These are a different
kind of "fudge", probably with superior characteristics, but less
"obvious" than the "square"-seeming impressive sounding "Gaussian"
grid. The name is apparently given by:
The gridpoints along the longitudes are equally spaced, while they are
unequally spaced along the latitudes, where they are defined by their
Gaussian quadrature. There are no grid points at the poles.
http://en.wikipedia.org/wiki/Gaussian_grid
and is concocted by:
http://www.ncl.ucar.edu/Document/Functions/Built-in/gaus.shtml
(This is similar to the "trick" used in sp plot methods for sp objects
in geographical coordinates, which stretches the y axis proportional
to the distance of the central y coordinate from the Equator).
It might be useful to be able to generate the correct spatial support
for these kinds of data, both point and polygon - summer exercise for
someone? Then they could be interpolated into fixed grids (NCAR
nomenclature).
This is a typical ontology question, where one research community
gives priority to its prefered view of the world, here climate
modellers prefering a representation that makes modelling easier, but
isn't graceful either with the world as it really exists, or with the
representations of other sciences.
Roger
Edzer - I'm using the T62 grids of temperature and precip to feed a
model. The other datasets in the workflow are in geographic CRS, so
I need to reproject the T62 data into geographic coordinates as well.
Since the T62 grid is nearly regular except with small variations
towards the poles, my current strategy is to ignore the changes in
cell size and pretend the grid is actually regular with a cell size
corresponding to that of the lower latitudes. I just thought it
would be more satisfying to be able to specify the CRS more precisely.
Best,
Matt
On 7/10/2010 2:22 PM, Edzer Pebesma wrote:
Matt, why exactly would you want to coerce these data into a
regular grid?
On 07/01/2010 04:05 PM, Matthew Landis wrote:
Dear R-sig-geo members,
I wonder if any of you have suggestions about how to import and use
rasters with a spectral Gaussian T62 CRS in R. I'm not very familiar
with this coordinate system (commonly used for climate models), and I
haven't been able to discover tools for dealing with them in R,
either
in PROJ.4 or mapproj or anywhere else, despite extensive scouring
of the
web. Details of Gaussian grids can be seen at
http://en.wikipedia.org/wiki/Gaussian_grid and
http://www.nco.ncep.noaa.gov/pmb/docs/on388/tableb.html#GRID98
Specifically, I'm trying to use output from the NCEP CFS climate
models
(see http://cfs.ncep.noaa.gov/cfs/monthly/tmp2m/ -- these are in GRIB
format). A different example of a T62 grid can be downloaded in
netCDF
format at http://www.sage.wisc.edu/iamdata/grid.php.
The goal is to transform the CRS into geographic lat/lon
coordinates to
be more compatible with our other datasets. The big problem with
getting these data into a SpatialGrid or SpatialPixelsDataFrame is
that
the latitude cells are not equally spaced - they get smaller
towards the
poles. I may be able to approximate it to a regular grid by ignoring
the changes to the poles (they are rather small after all), but I'd
rather do it properly if possible.
Code below shows extracting latitudes from a T62 grid and plotting
the
changes in cell size with latitude.
library('ncdf')
library('sp')
nc<- 'C:/scratch/cfs/pop_den_1995_T62.nc' # Downloaded from
http://www.sage.wisc.edu/iamdata/grid.php
# Open the file and get latitude and longitude
nc<- open.ncdf(ncfile)
lat<- get.var.ncdf(nc, varid = 'latitude')
lon<- get.var.ncdf(nc, varid = 'longitude')
close.ncdf(nc)
# Generate coordinates for each grid cell
x<- rep(lon, length(lat))
y<- rep(lat, each = length(lon))
sp<- SpatialPoints(cbind(x,y))
# temp<- SpatialPixels(points = sp) # Doesn't work. points have
to be
regular grid
# Show how the cell size changes with latitude
cell.size<- diff(rev(lat))
plot(lat[-1], cell.size)
Many thanks for any suggestions!
Matt