CDO

summary: I'd appreciate advice regarding tools and methods for transforming values from an unprojected global 3D spatial grid to a projected 3D grid with different horizontal and vertical resolution, or pointers to other resources to consult.

details:

I have output from a global atmospheric model that I'd like to use as initial and boundary conditions for a regional model. The global input netCDF has dimensions=2.5° lon x 1.875° lat x 56 vertical levels. The regional model runs over North America using a 12-km grid projected Lambert Comformal Conic (LCC), with 34 vertical levels. Since its top height is less than that of the global input, the extents of the output domain are fully contained within the input domain.

Each box or voxel defined by the global input grid contains an estimate for the N2O concentration for that volume. From those I want to compute the concentrations for each output gridbox volume. I have done this using R for 2D (aka "regridding") https://github.com/TomRoche/GEIA_to_netCDF , but I'm not seeing tools for the 3D usecase (aka "reboxing"). The best candidate I've seen so far is R package=@gstat, but (IIUC)

• gstat expects projected input. I'm not sure if I can work around that for this usecase.
• as the name implies, 'gstat' is doing geostatistics, e.g., variogram- and covariance-based modeling. I'm not sure either how to setup the distance weighting for my scenario, and, frankly, I remain unconvinced that a statistical approach is necessary for this application (though it may be a sufficient or the best-available approach). This may be due to my statistical ignorance, however.

I'd appreciate your recommendations for CDO tools that can handle this usecase; I'd especially appreciate example code.