We are happy to announce a new version of python-awips (v23.1) is available! This version is available via source code (with all example notebooks) or via mamba (conda) and pip. Please see our main documentation page for installation instructions.
This year's annual American Meteorological Society meeting was held 12-16 January 2025 in New Orleans, LA. Several NSF Unidata staff members were able to travel to New Orleans to visit with students, present papers and posters, and otherwise take part in the conference. As always, staff members spent some time meeting with community members at UCAR's exhibit hall booth. Read on for some of the conference highlights from the perspective of NSF Unidata staff.
FireWxPy is a user friendly, open source Python package to create visualizations of data specific to fire weather and fire weather forecasting, created by Eric J. Drewitz. The package is built to support a wide range of fire weather-focused visualizations for any state or GACC Region. Users can also create custom boundaries using latitude and longitude coordinates. Version 1.4.3, released February 1, 2025, allows users to create a variety of graphics, samples of which you can view in the full article.
Version 5.3.2 of the netCDF Operators (NCO) has been released. NCO is an Open Source package that consists of a dozen standalone, command-line programs that take netCDF files as input, then operate (e.g., derive new data, average, print, hyperslab, manipulate metadata) and output the results to screen or files in text, binary, or netCDF formats.
The NCO project is coordinated by Professor Charlie Zender of the Department of Earth System Science, University of California, Irvine. More information about the project, along with binary and source downloads, are available on the SourceForge project page.
The StatQuest Illustrated Guide to Neural Networks and AI: With hands-on examples in PyTorch!!! strikes an excellent balance between accessibility and technical depth. Josh Starmer, PhD, builds on his previous work while making neural networks approachable for both students and practitioners. This book has a similar feel and vibe to the previous book, The StatQuest guide to Machine Learning.
If you provide Earth Systems Science learning opportunities at the post-secondary levels or within the workforce, UCAR needs your input! UCAR would love to hear about any learning opportunities that you offer on emerging ESS capabilities — ranging from AI/ML and data management to relationship building, creativity, and more — that are needed in the workplace now and in the future. Help UCAR understand your priorities and any obstacles to providing education, training, and support for these capabilities.
