by Daphne Thompson | Oct 15, 2026 | Boxer
Weather data used to come from a relatively familiar list of sources: weather stations, radar, satellites, and numerical weather models. Today, that list is getting much longer. There are more sensors, observation networks, high-resolution models, machine learning...
by Daphne Thompson | Oct 1, 2026 | Boxer, Terrier
When most people open a weather app, they’re greeted by a familiar sight: a radar map showing rain and storms moving across the landscape. Radar has become synonymous with weather data, but it’s only one of many weather map layers that help people make...
by Daphne Thompson | Sep 3, 2026 | Data
During the summer of 2026, three meteorology students spent nearly three months building weather applications with AI. One developed a storm chasing dashboard packed with professional forecasting tools. Another created a fishing forecast application designed around...
by Daphne Thompson | Aug 20, 2026 | Terrier
This summer, three college students set out to answer the same question: How can AI help build better weather applications? During their internships at Wet Dog Weather, Alicia Skiver-Palmer, Lex Hudson, and Antonio McElfresh each developed unique AI weather...
by Daphne Thompson | Jul 30, 2026 | Data
Weather data is everywhere, but during severe weather, it’s often scattered across multiple websites and applications. That’s the problem Mississippi State University graduate student Alicia Skiver-Palmer set out to solve this summer by building an AI...
