Interpolating world data to a local grid in Python I have an NC file that has global data with a high resolution (about 25 minutes) so a lot of coordinates.
I have some measured values of some region with irregularly spaced coordinates.
In order to mathematically operate between the two, I am aiming to interpolate the global data to the local irregular grid, instead of the opposite, to minimize the uncertainty.
How do I achieve this? I was thinking of Scipy's griddata module but cannot seem to initiate the process. Thanks a lot.


EDIT:

I tried the following, a little heavy on resources but I think it works.

from scipy.interpolate import interp2d   
f = interp2d(longw,latw,Zw)


where longw,latw,Zw are the world coordinates of the NETCDF file and the 2D variable respectively.

Then I applied f(lon,lat) to all my irregular observations y iteration to interpolate. The lon and lat are the observed coordinates of the local region.
As @gansub implicitly suggested, you may have a better outcome by defining the output grid as well. 
Check this very similar question: 
How to interpolate scattered data to a regular grid in Python?

After comments, I realize I missed the irregular target grid. Maybe this answer helps:
https://stackoverflow.com/questions/3242382/interpolation-over-an-irregular-grid