Mastering MPAS (Model for Prediction Across Scales) grid data requires transitioning from traditional rectangular lat-lon grids to handling unstructured Voronoi (hexagonal) meshes. Because MPAS allows for variable-resolution modeling—meaning it can seamlessly zoom into high-resolution regional weather zones while keeping global contexts coarse—rendering this data requires specialized workflows. Understand the Dual Mesh System
MPAS operates on a Spherical Centroidal Voronoi Tessellation (SCVT) paired with its Delaunay triangulation dual mesh. Visualizing variables requires matching the parameter to the correct geometric structure:
Primal Grid (Hexagons): Best for cell-centered scalar atmospheric variables like relative humidity, temperature, or tracer concentrations.
Dual Grid (Triangles): Best for vector-derived or flow-based variables like vorticity or wind divergence, which are computed across the cell edges and vertices. Choose the Right Visualization Strategy
Depending on your compute resources and specific project goals, you can visualize MPAS data through two primary pathways: 1. Native Grid Plotting (High Accuracy)
Plotting directly on the native unstructured honeycomb grid preserves data integrity and captures fine-scale regional weather transitions perfectly without interpolation errors.
Use modern Python frameworks like uxarray or holoviews combined with cartopy to build native polygon plots.
For desktop GIS analysis, look into the NCAR Command Language (NCL) utilizing scripts like atm_cells.ncl to render native, color-filled cell polygons. 2. Lat-Lon Interpolation (High Compatibility)
If your legacy downstream analysis tools or web-mapping frameworks require structured rectangular data, you must remap the irregular mesh.
Use the official NCAR convert_mpas command-line utility or the mpas_to_latlon.ncl script to pre-compute bilinear interpolation weights.
Export the remapped grids directly into highly digestible standard NetCDF, GeoTIFF, or GRIB formats. Optimize Large Dataset Performance
Variable-resolution MPAS runs can produce massive files with tens of millions of grid cells, presenting major performance bottlenecks. 9. Visualization — MPAS Atmosphere documentation
Leave a Reply