NetCDF operators (NCO) version 5.3.4
17 June 2025
Version 5.3.4 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.
[Read More]Welcome Summer Intern Jaye Norman
05 June 2025
Jaye Norman joined the NSF Unidata Program Center as a student summer intern on May 19, 2025. This fall, Jaye will be a Senior at North Carolina State University in Raleigh. After finishing her Bachelor's degree in Meteorology, Jaye plans to attend graduate school for a Master's degree in Meteorology with a focus on numerical modeling.
[Read More]Welcome Summer Intern Linfeng Li
05 June 2025
Linfeng Li joined the NSF Unidata Program Center as a student summer intern on May 19, 2025. Linfeng is a PhD student in Climate and Space Sciences and Engineering at the University of Michigan, where his research focus is in Planetary Sciences. “I am modeling the atmospheric dynamics of ice giants and lava planets, studying the potential intrinsic asymmetry of planetary atmosphere,” he says. “I've always been excited to learn how diverse and distinct the planetary environments are.”
[Read More]netCDF-Java version 5.8.0 released
08 May 2025
The NSF Unidata THREDDS development team released netCDF-Java 5.8.0 on May 8th, 2025. This release contains a number of upgrades to third party libraries, a variety of bug fixes, and several new features and improvements.
[Read More]New eLearning: Supervised Machine Learning Readiness
07 May 2025
Announcing a new eLearning series available now on Unidata eLearning: Supervised Machine Learning Readiness. This learning series is a self-paced, beginner-friendly program designed for Earth systems scientists to explore the core principles of supervised machine learning. This series uses a combination of step-by-step frameworks, exploratory widgets, and low-code exercises in Jupyter Notebooks, to explore the full cycle of machine learning model development. No programming experience is required. By the end of the series, you will be able to recognize when machine learning is an appropriate tool and critically evaluate machine learning in Earth systems science contexts.
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