In this MetPy Monday tutorial, John Leeman from NSF Unidata continues the two-part series on temperature gradients in Python. In Part 1, the temperature gradient was calculated from HRRR 850-hPa temperature data using proper grid spacing. In Part 2, the focus shifts to turning that calculated gradient field into a clear, presentation-ready weather map.

This episode walks through building a map with Matplotlib and Cartopy, using filled contours to shade the temperature gradient magnitude and dashed contours to show the underlying 850-hPa temperature field. John explains why a simple Plate Carree map projection is useful here for speed, especially when contouring over a relatively small domain.

The tutorial also covers several practical plotting choices that make the final map easier to read, including adding state and county boundaries, adjusting line widths, adding a horizontal color bar, choosing useful contour levels, extending the color scale, switching colormaps, labeling temperature contours, and reducing clutter by changing county line color and transparency. The final result is a polished map showing where the strongest 850-hPa temperature gradients are located across the southern Plains case study.