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A new result based on the code below... not sure if it's better ;) It does seem to be an improvement, but the problem is I still ultimately need the data to be on a 0.5x0.5 degree grid. Also, this would clearly still need masking at some locations. # Set up a basemap and interpolate data fig,m = mp.get_base1(region='NPOLE') #transform to nx x ny regularly spaced native projection grid dx = 2.*np.pi*m.rmajor/len(lons) nx = int((m.xmax-m.xmin)/dx)+1; ny = int((m.ymax-m.ymin)/dx)+1 # Project data into basemap ## create interpolator print "creating interpolators" #Z = mlab.griddata(x,y,z,lons,lats) x,y = m(x,y) newx= np.arange(m.xmin,m.xmax,(m.xmax-m.xmin)/100) newy= np.arange(m.ymin,m.ymax,(m.ymax-m.ymin)/100) Z = mlab.griddata(x,y,z,newx,newy) http://www.nabble.com/file/p24926398/regrid_projection_first.png -- View this message in context: http://www.nabble.com/2d-interpolation%2C-non-regular-lat-lon-grid-tp2490968... Sent from the Scipy-User mailing list archive at Nabble.com.