AWIPS Tips: Customized Contours
20 March 2024
Welcome back to AWIPS Tips!
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.
[Read More]Exploring Indigenous Data Sovereignty
06 March 2024
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.
[Read More]AWIPS Tips: Exploring Satellite Imagery using Python-AWIPS
06 March 2024
Welcome back to AWIPS Tips!
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.
[Read More]NSF Unidata 2024 Community Equipment Awards: Deadline Extended
04 March 2024
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.
[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]
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