Several current and former NSF Unidata Program Center staff members have been recognized with the American Geophysical Union's 2024 Open Science Recognition Prize for their work as part of a team of Major Contributors to the CF Conventions.

NSF Unidata offers JupyterHub resources tailored to the instructional requirements of university atmospheric science classes through the Science Gateway project. For the Spring 2024 term, NSF Unidata is once again offering universities (or individual instructors) access to cloud-based JupyterHub servers tailored to their requirements.

Representation of nodes in a Self Organizing Map.

A self-organizing map (SOM), sometimes known as a Kohonen map after its originator the Finnish professor Teuvo Kohonen, is an unsupervised machine learning technique used to produce a low-dimensional representation of a higher dimensional data set. SOMs are a specific type of artificial neural network, but use a different training strategy compared to more traditional artificial neural networks (ANNs). SOMs can be used for clustering, dimensionality reduction, feature extraction, and classification — all of which suggest that they can be important tools for understanding large Earth Systems Science (ESS) datasets.

Since 2018, Unidata has been offering JupyterHub resources tailored to the instructional requirements of university atmospheric science classes through the Science Gateway project. For the fall 2023 term, Unidata is once again offering universities (or individual instructors) access to cloud-based JupyterHub servers tailored to the requirements of university atmospheric science courses and workshops. Unidata will work with you to customize the technologies and data requirements for your class. By using the Unidata Science Gateway, instructors can add Jupyter notebooks used in their coursework to a dedicated JupyterHub hosted using Unidata's resources in the NSF Jetstream cloud. Once logged in to the JupyterHub, individual students access pre-configured computing environments that allow them to work with the notebooks interactively, making and saving their own alterations to existing notebooks or creating their own new notebooks.

Machine Learning systems are often configured around Graphics Processing Units (GPUs) rather than Central Processing Units (CPUs). Why should this be the case, in an era when CPUs are powerful and (relatively) inexpensive? This article provides some insights into what GPUs are and why they provide advantages for certain types of computations, including some commonly used for machine learning and modeling.