In this MetPy Monday tutorial, John Leeman from NSF Unidata demonstrates how to access GOES satellite archive data with MetPy and create a satellite image using MetPy’s simplified, declarative plotting interface. This episode uses GOES-East / GOES-19 data from the April 11, 2026 Kansas supercell case and focuses on plotting ABI Band 2 visible imagery in Python.

The tutorial walks through using MetPy’s remote data access tools to find a GOES product near a target time, download the file locally as NetCDF, open the dataset with Xarray, inspect the available data variables, and plot the cloud moisture imagery field. Instead of manually building the entire Matplotlib and Cartopy map setup, John shows how MetPy’s declarative plotting interface can use the projection and metadata already stored in the Xarray dataset to simplify the plotting workflow.

You will learn how to create an ImagePlot, place it on a MapPanel, add map layers like states, borders, and coastlines, set a focused map area over the Kansas storm, and render the final image using a PanelContainer. This is a practical workflow for quickly turning archived GOES satellite data into useful case study graphics.