As a result of changes in the spring 2024 meeting schedule for the NSF Unidata Users Committee, we are able to extend the submission deadline for this year's Community Equipment Awards solicitation until March 29, 2024. All other aspects of the 2024 program remain as described in the original announcement.
K Nearest Neighbors (KNN) is a supervised machine learning method that 'memorizes' (stores) an entire dataset, then relies on the concepts of proximity and similarity to make predictions about new data. The basic idea is that if a new data point is in some sense 'close' to existing data points, its value is likely to be similar to the values of its neighbors. In the Earth Systems Sciences, such techniques can be useful for small- to moderate-scale classification and regression problems.
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.
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).
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.
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.
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.
