https://github.com/ugrid-conventions/ugrid-conventions
" The CF-conventions are widely used for storing and distributing environmental / earth sciences / climate data. The CF-conventions use a data perspective: every data value points to the latitude and longitude at which that value has been defined; the combination of latitude and longitude bounds and cell methods attributes can be used to define spatially averaged rather than point values.
This is all great for the distribution of (interpolated) data for general visualization and spatial data processing, but it doesn’t capture the relationship of the variables as computed by a numerical model (such as Arakawa staggering). Many models use staggered grids (using finite differences, or finite volume approach) or use a finite element approach of which the correct meaning may not be captured easily by simple cell methods descriptors. This becomes a problem if you don’t want to just look at the big picture of the model results, but also at the details at the grid resolution:
- What is the exact meaning of a flux on the output file in discrete terms?
- Can we verify the mass balance?
- Can the data be used for restarting the model?
In this context we have created these lightweight SGRID conventions to define the core aspects of a structured staggered grid without trying to capture the details of finite element formulations. This is an attempt to bring conventions for structured grids on par with those for unstructured grids.
https://sgrid.github.io/sgrid/
https://github.com/sgrid/pysgrid
https://github.com/NOAA-ORR-ERD/pyugrid
https://docs.google.com/presentation/d/1E4oZMMi7bs2MonV7kDQzHyUWjwxyN29yR4GSzWjA2Kw/edit#slide=id.p4
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