Hi @ChrisBarker-NOAA,

This puzzled me for a bit, until I remembered that in the CF data model, DSGs 
are _not_  special. This is because what looks like a discrete axis in the 
encoding of a DSG is a really just a form of lossless packing for an orthogonal 
multidimensional array (with the data and coordinate arrays padded with missing 
data if required). This is indeed different to the UGRID mesh, as you rightly 
picked up on.

So bringing DSG's into this was misleading - sorry!

The [all that the CF data model says about 
DSGs](https://urldefense.us/v3/__https://cfconventions.org/cf-conventions/cf-conventions.html*_domain_axis_construct_and_the_data_array__;Iw!!G2kpM7uM-TzIFchu!kpIRMVftXZrAcR1WIvvNZ6ctbN1jxRTEXLA68dt7V_fuRrMa6_J0r1EyT1N_goBO3Cqr0opq5P4$
 ) is:

_When a collection of discrete sampling geometry (DSG) features has been 
combined in a data variable using the incomplete orthogonal or ragged 
representations to save space, the axis size has to be inferred, but this is an 
aspect of unpacking the data, rather than its conceptual description. In 
practice, the unpacked data array may be dominated by missing values (as could 
occur, for example, if all features in a collection of time series had no 
common time coordinates), in which case it may be preferable to view the 
collection as if each DSG feature were a separate variable, each one 
corresponding to a different field construct._

In my other examples (such as data stored on ocean basins) there is no 
assumption of (spatial) continuity, so I reckon that the CF data model discrete 
axis does apply to the UGRID case, after all.

Thanks,
David

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