Title:
SIFT: An OpenGL-powered Python GUI for Training NWS Forecasters

Description:
Advances in meteorological satellite instrument technology can produce higher resolution imagery than previously available to the scientific and forecasting communities. This imagery is increasingly difficult to analyze with traditional visualization software due to its large size. In an effort to provide a fluid, lightweight, and easy-to-use analysis experience for forecaster training courses, the Satellite Information Familiarization Tool (SIFT) was created. SIFT is a PyQt application built on VisPy for visualizing satellite imagery with various data analysis and compositing features. This poster provides an overview of SIFT’s feature set and how it is used to train weather forecasters.



Presenter(s):
David  HoeseSpeaker: David Hoese, University of Wisconsin - Madison, Space Science and Engineering Center
 Speaker: Ray Garcia, University of Wisconsin - Madison, Space Science and Engineering Center
 Speaker: Jordan Gerth, University of Wisconsin - Madison, Space Science and Engineering Center
 Speaker: Scott Lindstrom, University of Wisconsin - Madison, Space Science and Engineering Center