NSF Unidata is governed by its community. Our Strategic Advisory and Users committees facilitate consensus-building for future directions of the NSF Unidata Program and establish standards of involvement for the community. Direct involvement in the Program by the academic community helps NSF Unidata stay on top of trends in Earth Systems Science education and research; for example, recent initiatives on Python and cloud-based computing have benefited tremendously from committee advice and involvement.
Registration is open for the 2024 Pythia Cook-off! This U.S. National Science Foundation-funded hackathon for Cookbook development will grow participants' Python coding, communication, collaboration, and educational development skills, while expanding the collection of Pythia Cookbooks for the open source, open science community. Pythia Cookbooks are crowd-sourced collections of domain-specific tutorials and exemplar workflows, building on existing Pythia Foundations tutorials. Cookbooks are supported by a rich GitHub-based infrastructure enabling collaborative authoring and automated health-checking to ensure reproducibility.
This week we are going to look at how to customize contours for products in CAVE by changing the styleRules. Customizations include adjusting the color, line type, smoothing, interval, and range by creating a user override of the d2dContourStyleRules.xml file. We will walk through the different options and show an example of a customized contour for model surface temperatures.
The concept of Indigenous Data Sovereignty (IDS) asserts that data generated by Indigenous peoples, including data generated from their land and resources, should be governed by the people themselves. Environmental observations collected on native lands are one small part of the IDS context, and they were the subject of a recent workshop hosted by the Southwestern Indian Polytechnic Institute (SIPI) in Albuquerque, New Mexico.
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.
