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 > _______________________________________________ CF-metadata mailing list [email protected] http://mailman.cgd.ucar.edu/mailman/listinfo/cf-metadata
