SSEC Unidata Server Shutting Down in April 2024
02 April 2024
The NSF Unidata server hosted at the Space Science and Engineering Center (SSEC)
at the University of Wisconsin, Madison (unidata3.ssec.wisc.edu)
will be permanently decomissioned on April 26, 2024.
Those using services provided by unidata3.ssec.wisc.edu can switch
to using the servers described in this article.
Shutdown of Two Special-Purpose THREDDS Data Servers
29 March 2024
NSF Unidata will be shutting down two existing special-purpose THREDDS Data Servers on April 15, 2024. Please read the full article for details.
[Read More]04 March 2024
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.
[Read More]Quick Tips for ESS Machine Learning Projects
12 February 2024
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.
[Read More]R2: Downsides and Potential Pitfalls for ESS ML Prediction
20 December 2023
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”).
[Read More]
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