In this MetPy Monday tutorial, John Leeman from NSF Unidata shows how to use Herbie to download HRRR model data and create a forecast radar plot in Python. This episode focuses on retrieving a specific HRRR model run, extracting composite reflectivity, and plotting forecast radar imagery with Matplotlib, Cartopy, and MetPy’s radar color tables. 

The tutorial uses the April 19–20, 2023 severe weather case and downloads the 18Z HRRR run with a 6-hour forecast, giving a valid time around 00Z on April 20. John demonstrates how Herbie simplifies model data access by knowing where common weather model files are stored, making it easier to request the data you need without manually building archive URLs.

After retrieving the HRRR surface product, the video shows how to use Xarray to access the composite reflectivity field, squeeze extra dimensions, extract latitude and longitude, convert longitudes from 0–360 degrees to -180–180 degrees, and build a forecast radar map. The final plot uses pcolormesh, Oklahoma county outlines, state borders, a reflectivity color table, and a labeled color bar to create a clean forecast radar visualization.