This week we're going to dive into a little bit of python-awips to learn more about what satellite data our EDEX has to offer. If this is your first time joining us, it may be helpful to take a quick glance over some of our previous AWIPS Tips blogs about python-awips. To take a deeper look into satellite data, we'll be highlighting some of the features and cells of the Satellite Imagery example notebook.
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.
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.
Do you know someone in the Unidata community who has been actively involved and helpful to you and other Unidata members? Perhaps this is someone who volunteers to assist others, contributes software, or makes suggestions that are generally useful for the community.
The Unidata Users Committee invites you to submit nominations for the Russell L. DeSouza Award for Outstanding Community Service. This Community Service Award honors individuals whose energy, expertise, and active involvement enable the Unidata Program to better serve the geosciences. Honorees personify Unidata's ideal of a community that shares ideas, data, and software through computing and networking technologies.
You may have noticed a change on this web site recently: where you might expect to see the name "Unidata" you are now beginning to see "NSF Unidata" in its place. Just what's going on?
