We at NSF Unidata are pleased to announce that we have now received funding from the National Science Foundation (NSF) for the next year of the period of performance of our five-year award. This positive development allows us to end the current furlough of our staff and resume our operations. While we are grateful to receive our next increment of funding, we are mindful of the challenges that lie ahead with our continued funding given the administration's proposed FY26 budget that cuts NSF's budget by over fifty percent.
Due to the current gap in funding from the U.S. National Science Foundation (NSF), the NSF Unidata Program is pausing most operations. Nearly all staff will be furloughed until funds from our existing NSF grant become available.
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.
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.
At NSF Unidata, we have successfully implemented and re-used weights from several global AI-NWP (Artificial Intelligence-Numerical Weather Prediction) models (FourCastNet, Pangu) using the NVIDIA earth2mip package. We can confirm that these models are open source and can be reused on high-end, but increasingly standard, HPC hardware. While traditional numerical weather prediction requires massive supercomputing resources, these AI models can potentially deliver similar or better results using standard GPU hardware for inference.
As a community-focused program, NSF Unidata relies on input from educators, researchers, students, and professionals working across the Earth system sciences. Whether you're a longtime user or new to our offerings, your voice plays a critical role in shaping the future of NSF Unidata.
