Is there a straightforward way to extract a region from an Iris cube which is described by 2D latitude and longitude variables, for example using NEMO ocean model data?
I found this workaround but was wondering if there was a way to do this in 'pure' Iris, without having to resort to defining a new function?
For example, if I have this cube...
In [30]: print(cube)
mole_concentration_of_dimethyl_sulfide_in_sea_water / (mol m-3) (time: 780; cell index along second dimension: 330; cell index along first dimension: 360)
Dimension coordinates:
time x - -
cell index along second dimension - x -
cell index along first dimension - - x
Auxiliary coordinates:
latitude - x x
longitude - x x
... and then try to extract a region using intersection, I get this...
>>> subset = cube.intersection(longitude=(-10, 10))
CoordinateMultiDimError: Multi-dimensional coordinate not supported: 'longitude'
Thanks!