Title:
SIFT: An OpenGL-powered Python GUI for Training NWS ForecastersDescription:
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):
![]() | Speaker: 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 |
