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Regridding: Interpolate to lat/lon grid first. (both PG2 and GLL data): 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. Mapping files are collected on the E3SM inputdata server: https://web.lcrc.anl.gov/public/e3sm/mapping/maps/

GLL vertex data: triangulation + dual grid cell fill. (ugly): This is the standard option in NCL and pyNGL for plotting unstructured data. NCL will can internally compute a Delaunay triangulation of the data (via http://www.cs.cmu.edu/~quake/triangle.html ), where the data now lives on the vertex of each triangle. NCL then appears to form the Voronoi diagram (dual grid of the triangulation). On this dual grid, the data is now located at the center of each Voronoi cells. Alternatively, the user can read in a dual grid from a SCRIP file and specify the polygon containing each GLL node. With “RasterFill”, each of these Voronoi dual grid cells will be colored based on the value of the original data set. This results are an accurate representation of all the data points in the original field, but can be quite ugly and is a very unnatural representation of the original spectral element data.

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For PG2 data, to remap from FV (unstructured) to FV (latlon), we dont yet have an integrated bilinear option. ESMF’s “aave” option ( which can be obtained most efficiently using NCO’s ncremap) is conservative and good for downscaling, but will produce blocky plots when upscaling. For nicer looking plots when upscaling, ESMF’s “bilinear” is currently the only option.

Dual grid

pyNGL can construct this dual grid from a triangulation if the user specifies sfXArray and sfYArray. WE dont conver this option here, but instead cover the second option where the user reads a dual grid from the SCRIP file and specifies that via sfXCellBounds and sfYCellBounds. For PG2 data, this is the most natural approach, with the PG2 dual grid made up of uniform quadrilaterals given in the PG2 SCRIP file.

For GLL data, the dual grid approach is less natural, because the data on the Gauss-Lobatto-Legendre (GLL) grid exists at cell vertices (i.e., data is point values), while most existing analysis tools expect cell-centered data (i.e., data points represent values over some representative area bounded by each “cell”). We can sort of mimic this cell-centered view by constructing a new mesh that is a “finite volume dual grid” to the SE GLL grid, in which we construct bounding polygons around each GLL node and pretend that the data at the GLL node is representative of the value across the area of the polygon. There are two approaches to constructing this dual grid: One can read the information from the GLL SCRIP file used to construct mapping files, or the GLL data can be triangulated, and then the dual grid of the triangulation (Voronoi grid) is constructed. For the Voronio approach, one sets sfXArray and sfYArray with the GLL node locations and the triangulation and Voronio grid will be computed internally.

For the SCRIP approach, an example SCRIP file can be found in the inputdata repository for the ne4np4 grid here: https://web.lcrc.anl.gov/public/e3sm/mapping/grids/ne4np4-pentagons_c100308.nc

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