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The most common approach is to interpolate the data to a FV latitude longitude grid, either “cap” or offset: CAM-FV Grid Overview . This allows one to use a wide variety of visualization software. Offline interpolation is quick and painless with NCO’s ncremap.
For GLL data, the best algorithm to use (see Transition to TempestRemap for Atmosphere grids ) is TempestRemap’s “intbilin”, as this map is accurate, monotone, and can be used for both downscaling and upscaling. The only drawback is that it is not exactly conservative. If exact conservation is needed (so that the mass computed on the interpolation grid is the same as on the native grid), then TempestRemap’s less accurate “mono” should be used.
For PG2 data, to remap from FV (unstructured) to FV (latlon), we dont yet have a an integrated bilinear option. ESMF’s “aave” option ( which can be obtained most efficiently using NCO’s ncremap) is conservative and good for downscalingsdownscaling, but will produce blocky plots when upscaling. For nicer looking plots when upscaling, ESMF’s “bilinear” is currently the only option.
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todo plot: Example: standard test case
4. PG2 data: triangularization +
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Voronio cell fill (unnatural)
NCL’s Delaunay approach can also be used for PG2 data: computing a Delaunay triangulation and then the associated dual Voronoi grid. However, this is a lot of work to construct a dual grid mesh, Voronoi cells when in the PG2 case, the data is already given as a cell average over nice quadrilaterals. For PG2, and using these PG2 quadrilaterals should be used for the dual grid when plottingquadrilaterals instead of Voronio cells (next option below) is a more accurate representation of the discrete data.
todo: plot Example : standard test case.
5. PG2 data: Cell fill using FV quads. (best for PG2 data)
NCL has an option a CellBounds option which allows the user to specify the dual grid (polygon cell containing each data point) and it will use this grid for plotting. With FV cell bounds for cell-centered data such as PG2. This option, combined with “CellFill”, each PG2 quad will be colored based on the value of the data point. This will result in a blocky plot, that correctly conveys the cell average nature of each data point. All values in the original data set will be preserved.
Unfortunately this option is not directly supported with matplotlib?
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