MATLAB: Find nearest grid cell between two 2D matrixes of different resolution

coarsedecimationfineinterp2latitudelongitudematrixregridresolution

Hello,

I have two matrices I would like to compare.

A is 192(lon) x 288(lat) B is 360(lat) x 720(lon)

How can I regrid B so that its resolution is the same as A?

I would like to know how to do this both by finding the nearest B lat&lon&values for A, and also by decimating B to the coarser resolution of A

Thank you,

Melissa

Best Answer

Assuming that they are regular grids with known latitude and longitude vectors, Along, Alat, Blong, Blat, then the approximation that neglects curvature of the Earth would be

[X, Y] = meshgrid(Blat, Blong);
[Xq, Yq] = meshgrid(Alat, Along);
Bapprox = interp2(X, Y, B, Xq, Yq);

The interp2() would need the longitude vectors phase-unwrapped if they cross 180 to -180:

degrad = pi./180;
unwrappedBlong = unwrap(Blong .* degrad) ./ degrad;

If you do need to take the curvature of the Earth into account, then possibly mapprofile might turn out to be usable for you -- I am too tired at the moment to figure out what a refmatrix is.

If mapprofile is not suitable then perhaps someone has already built a routine. If not then intrplon might help.

Related Solutions

MATLAB: Interpolation problem between two 2D global datasets

There is a major discontinuity in the middle of the longitude data, that makes interpolation impossible. Lets find it:

>> median(diff(lat_model))
ans =
  0.9424
>> all(0<diff(lat_model)) % this is monotonic, so no problem
ans =
     1
>> median(diff(lon_model))
ans =
  1.2500
>> all(0>diff(lon_model)) % not monotonic!
ans =
     0
>> tmp = diff(lon_model);
>> find(tmp<0) % lets find where...
ans =
   145
>> lon_model(140:150) % and the data looks like this:
ans =
  173.7500
  175.0000
  176.2500
  177.5000
  178.7500
  180.0000
 -178.7500
 -177.5000
 -176.2500
 -175.0000
 -173.7500

Wow... it jumps from +180 to -178.75, in the middle of a monotonic increasing sequence.

How to deal with this is up to you, and depends on what the data represents. If these are degrees you may be able to convert them to the positive equivalents:

idx = lon_model<0;
lon_model(idx) = 360 + lon_model(idx);

You should also remove that transpose from first_model, which will cause another error. This code works without error:

load data
idx = lon_model<0;
lon_model(idx) = 360 + lon_model(idx);
[X, Y] = meshgrid(lat_model, lon_model); %first_model coordinates
[Xq, Yq] = meshgrid(lat, lon); %Serna coordinates
Serna_approx = interp2(X, Y, first_model, Xq, Yq);

although this is not proof that it does what want it too

MATLAB: Interpolation of Data on Different Grids

The best way is to project the elevation data using an appropriate projection. Then interpolation should be no problem with interp2. Choosing a projection will depend on where the data lie on the globe (near the equator?, near the poles?) and how large an area the data spans. If your bathymetry data spans only a few hundred kilometers, perhaps a UTM projection is appropriate. There's a pretty good explanation of projections here. If you have the mapping toolbox, you can project and unproject with projfwd and projinv.

Another option, for a small area of the globe, could be to use interpm2 in the lat dimension to get an array of lats, use it again for the lon dimension, and call that your new grid. But again, this would only be appropriate for a small area of the globe where spacing between meridians does not change very much over the north-south extents of the data.

Best Answer

Related Solutions

MATLAB: Interpolation problem between two 2D global datasets

MATLAB: Interpolation of Data on Different Grids

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