Dear Rich and Seth
> 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.)
In fact they don't have to be included at all. It is OK just to have dimensions
without coordinate variables.
Auxiliary coordinate variables are not supposed to have missing data values.
However, we relaxed that requirement in CF 1.6 for discrete sampling
geometries. We could relax it generally, but I suppose it would make sense
to permit missing data in aux coord vars only at points where the data
variable(s) concerned has missing data.
Best wishes
Jonathan
> 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
> >
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Received on Thu Feb 06 2014 - 09:42:30 GMT
