In this dataset, lat and lon aren't coordinate variables, they're
auxiliary coordinate variables. There's no requirement that auxiliary
coordinate variables be monotonic or lacking in missing_values. Your
coordinate variables will be the ones associated with the I and J
dimensions of the arrays.
To make this data CF compliant, I believe all you'll have to do is:
1) Make sure there are coordinate variables for the I and J dimensions.
(These can just have nondimensional dummy values.)
2) List the lat and lon variables in the coordinate attribute of each
data variable.
Cheers,
--Seth
On 2/5/14 1:49 PM, Signell, Richard wrote:
> CF folks,
>
> Many ocean models have curvilinear grids, but most of the ones I've
> encountered have 2D coordinate variables lon,lat that are monotonic
> and have no missing_values.
>
> The NOAA forecast model for the St. Johns River Operational Forecast
> System, however, has a complex geometry, and used a grid generator
> that allowed for "cuts" in the grid, so that in the resulting grid
> system lthe coordinate variables on/lat are not monotonic and have
> missing values.
>
> I did an experiment to see if I could fill in the missing lon/lat
> values via interpolation, but this exercise only made it clear that
> this cannot work, as the data being interpolated are not monotonic.
>
> Check out the first figure here to see the issue:
> https://www.wakari.io/sharing/bundle/rsignell/SJROFS
>
> Is there any way this operational forecast data could be made to be CF
> compliant?
>
> Thanks,
> Rich
>
Received on Wed Feb 05 2014 - 14:09:25 GMT
