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AWIPS Tips: AWIPS Full Beta Release v23.4.1-0.3

25 July 2024

Welcome back to AWIPS Tips!

AWIPS 23.4.1-0.3 is a beta release, with both EDEX and CAVE installation options. This release includes a major upgrade for the operating system, running on Rocky 8 Linux – which is a free Red Hat Enterprise Linux (RHEL) distribution.

This release is still in beta since the National Weather Service is still working on their 23.4.1 release to operations and does not have full functionality. Users will have the option of installing their own EDEX with this version, or continue to connect to Unidata's public EDEX (edex-beta.unidata.ucar.edu).

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Posted by Tiffany Meyer [ Comments [0] ]

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NSF Unidata Funding Proposal Approved by U.S. National Science Foundation

16 July 2024

The NSF Unidata Program receives the majority of its funding from the U.S. National Science Foundation. Every five years, the program submits a new proposal for core program funding to the NSF, outlining past accomplishments and describing plans for future activities.

We are please to announce that our most recent five-year funding proposal, Unidata Reimagined: New Approaches to Community Data Services, has been awarded.

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Posted by Unidata News [ Comments [0] ]

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NSF Unidata at the 2024 Earth Educators' Rendezvous

15 July 2024

NSF Unidata is at the 2024 Earth Educators' Rendezvous in Philadelphia, PA the week of July 15-19, 2024. Join Instructional Designer Nicole Corbin and AI/ML Software Engineer Thomas Martin on Friday's poster session to discuss their ongoing collaboration with Metropolitan State University of Denver, Machine Learning Foundations and Applications in the Earth Systems Sciences.

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Posted by Nicole Corbin [ Comments [0] ]

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New eLearning: Machine Learning Foundations in the Earth Systems Sciences

12 July 2024

Announcing a new eLearning module available now on Unidata eLearning: Machine Learning Foundations in the Earth Systems Sciences. This no-code module is designed to guide you through the very basics of supervised machine learning in the Earth Systems Sciences. You will discover how machine learning is currently being used by scientists, examine the process for supervised machine learning model development, explore how data plays a crucial role in making good predictions, and how to be an effective and ethical user of machine learning tools. You will also learn that machine learning is not a catch-all solution to every problem!

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Posted by Nicole Corbin [ Comments [0] ]