In this week's AWIPS Tips, we're reflecting on a great year of sharing tips, resources, and announcements with you, the Unidata AWIPS community. All of our posts from 2022 are catalogued below. We're excited to share even more with you in 2023!
NSF Unidata offers JupyterHub resources tailored to the instructional requirements of university atmospheric science classes through the Science Gateway project. For the Spring 2024 term, NSF Unidata is once again offering universities (or individual instructors) access to cloud-based JupyterHub servers tailored to their requirements.
Regression analysis is a fundamental concept in the field of machine learning (ML), in that it helps establish relationships among the variables by estimating how one variable affects the other.
The coefficient of determination, R2 (pronounced “R squared”), is a measure that provides information about how well the regression line suggested by a numerical model approximates the actual data (often referred to as “goodness of fit”).
We are excited to announce our production release of AWIPS 20.3.2-1. This release includes installers for CAVE (CentOS7, Windows, VMware Player, and MacOS), and for EDEX (CentOS7). This release uses RHEL7 (CentOS7), Java11 and Python3.
Do you use NSF Unidata software packages? Do you love to write code or teach others about data-centered Earth System Science? Maybe you're just interested in the interplay of science and data? The NSF Unidata Summer Internship program is looking for you!
The NSF Unidata Summer Internship offers undergraduate and graduate students an opportunity to work with NSF Unidata Program Center staff on projects drawn from a wide variety of areas in the atmospheric and computational sciences. NSF Unidata's mission is to support the Earth System Science research and education community with data and tools for data access, analysis, and visualization. As a NSF Unidata intern, you'll pursue the goal of adding innovative enhancements to data access, analysis, and visualization tools developed within NSF Unidata.
A self-organizing map (SOM), sometimes known as a Kohonen map after its originator the Finnish professor Teuvo Kohonen, is an unsupervised machine learning technique used to produce a low-dimensional representation of a higher dimensional data set. SOMs are a specific type of artificial neural network, but use a different training strategy compared to more traditional artificial neural networks (ANNs). SOMs can be used for clustering, dimensionality reduction, feature extraction, and classification — all of which suggest that they can be important tools for understanding large Earth Systems Science (ESS) datasets.
The Department of Earth System Science (ESS) within the School of Physical Sciences at The University of California, Irvine (UCI), is seeking candidates for two positions.
