Last Monday was a big day for folks in the geoscience and astrosciences — the 2017 total solar eclipse! Many of those on the Unidata team made the drive to be in the path of totality, where the sun was completely blocked for a period of up to two and a half minutes. In this MetPy Monday post, we will take a look at some animations made in Python and posted by the team just after the eclipse.

I recently installed a weather station in my back yard. Every day I look at the display and see the temperature, wind, rain, and humidity, but the dewpoint is not displayed by default! As it turns out, dewpoint is a tricky thing to directly measure. The only way to directly measure it is with a fogged mirror sensor. Otherwise a hygrometer or psychrometer can be used to measure humidity or wet-bulb temperature, and then the dewpoint can be calculated. MetPy has the calculation functions to do both of these conversions. In this week’s MetPy Monday I’ll show you how to use the Jupyter Notebook’s interactive widgets to make a dewpoint calculator with slider widgets. This is a great way to get students to interact with formulas and get an intuitive sense of how they work!

Last week we looked at how to create a simple base map with Cartopy. In this week’s MetPy Monday, we learn about contouring a field on the map and some of the idiosyncrasies of cyclic points. In the end, we will have a plot of the globe with the Coriolis parameter contoured. You can use this functionality to create height maps and more!

We’ll start off with importing the tools we will use: matplotlib, MetPy calculations, MetPy units, and numpy. We’re also using the magic %matplotlib inline so figures show up in the notebook instead of in separate windows.

One of the most common tasks we do as geoscientists is make maps. Maps are a great way to look at massive amounts of data and synthesize it. In Python, Cartopy is the most current mapping package available and is what we use in all of our MetPy Gallery and Python Gallery examples. In this week’s MetPy Monday, we’ll look at the fundamentals of mapping with Cartopy and create a couple of simple base maps that data can be plotted on.