NetCDF operators (NCO) version 5.2.1

Version 5.2.1 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.

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AWIPS Tips: AWIPS 20.3.2-2 Software Release

Welcome back to AWIPS Tips!

We are excited to announce our release of 20.3.2-2 that incorporates many updates and fixes from the 20.3.2-1 release. This release includes installers for CAVE (CentOS7, Windows, VMware Player, and MacOS), and for EDEX (CentOS7).

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AMS 2024 Conference Highlights from the NSF Unidata Staff

AMS 2024 Annual Meeting

This year's annual American Meteorological Society meeting was held 27 January - 1 February 2024 in Baltimore, MD. Several NSF Unidata staff members were able to travel to Baltimore to lead workshops, 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. The following are some of the conference highlights from the perspective of NSF Unidata staff.

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ESIP: Raskin Scholarship

ESIP

The Federation of Earth Science Information Partners (ESIP) is an open networked community that brings together science, data and information technology practitioners around Earth science issues.

The Raskin Scholarship is open to current graduate students in Earth or computer sciences who has an interest in community evolution of Earth Science data systems. Preference is given to applicants who can demonstrate a connection to ESIP-related activities.

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Quick Tips for ESS Machine Learning Projects

Your idea of what's entailed in setting up a supervised Machine Learning (ML) project as an Earth Systems scientist is probably not as fanciful as what an image generation algorithm came up with. But there are many little decisions ML practitioners make along the way when starting an Earth Systems Science (ESS) ML project. This article provides some tips and ideas to consider as you're getting started. These tips are not in any particular order, and like all things related to ML projects they depend on the specific types of data and project goals.

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News and information from the Unidata Program Center
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News and information from the Unidata Program Center

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