In this MetPy Monday tutorial, John Leeman from NSF Unidata begins a two-part series on making meteorological cross-sections in Python using MetPy. In Part 1, the focus is on preparing the data and creating a top-down map view of the cross-section path before building the actual vertical cross-section plot in Part 2.

This episode walks through downloading HRRR model data for the April 20, 2023 severe weather case, opening GRIB2 data with Xarray and cfgrib, parsing metadata with MetPy, manually assigning the HRRR Lambert conformal conic projection, and creating projected x/y coordinates for later cross-section interpolation. The tutorial then defines a start and end point for the cross-section and plots that path on a simple Cartopy map, running from Texas through Oklahoma, Kansas, and Nebraska.

This video is especially useful if you want to understand the setup steps required before using MetPy’s cross_section tools. Rather than jumping straight to the finished vertical slice, John shows how to inspect the model data, handle missing coordinate reference system information, and verify the cross-section location in map view.