My summer internship at NSF Unidata has been a fulfilling and unforgettable experience. For the majority of the summer, I dedicated my time to two primary projects: accelerating the thermodynamics module of MetPy by swapping its Python core with C++, and creating a 3D visualization of Jupiter’s mid-latitude jet streams using NSF Unidata’s Integrated Data Viewer (IDV).

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When presented with a list of possible project ideas for my NSF Unidata summer internship, one that caught my eye was the benchmarking of MetPy using the open-source software airspeed velocity (asv). I had never worked in a Continuous Integration/Continuous Deployment (CI/CD) context before, and expanding my knowledge of this DevOps concept seemed intriguing.

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Bringing soundings to life! This week’s #MetPyMonday shows how to animate Skew-T diagrams + build a growing CAPE time series using MetPy + Siphon.

Keeping your weather data organized and efficient is key when working with large atmospheric datasets. In this week's MetPy Monday, we show how to build a smart sounding data cache in Python—complete with fallback data sources and automatic cleanup of old files. You'll learn to streamline your workflow by caching soundings locally, minimizing downloads, and clearing out unused data. This simple system keeps your storage tidy while making your weather data instantly accessible.

Tired of downloading the same sounding data over and over? This week we show you how to build a local cache for NWS soundings using Python's Path library and Siphon! Learn how to automate your data retrieval and streamline your workflow in just a few minutes.