In this week's MetPy Monday, we show you how to find the closest weather observation station to any location using a combination of: MetPy's built-in station metadata, scipy.spatial.KDTree for fast spatial search, and GeoPandas for accurate geodesic distance calculation.

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.

Wind roses are a powerful tool for visualizing wind patterns, but what if you could overlay them on real-world maps for better geographic context? In this week's MetPy Monday, we take wind data analysis to the next level by plotting wind roses on OpenStreetMap layers using CartoPy. By integrating Python’s windrose package with CartoPy image tiles, we create a geospatial visualization that combines meteorology and mapping.

In this tutorial, you’ll learn how to extract wind speed and direction data, generate wind rose plots, and overlay them onto OpenStreetMap using customized map layers. Whether you're working in meteorology, environmental analysis, or GIS, this approach provides deeper insight into regional wind patterns. Watch the full video to see how Python can help you bridge the gap between weather data and spatial analysis!

Learn how to plot wind roses using Python!