Today on the show we have Elizabeth Barnes, Associate Professor in the department of Atmospheric Science at Colorado State University, who joins us to talk about her work Identifying Opportunities for Skillful Weather Prediction with Interpretable Neural Networks. Find more from the Barnes Research Group on their site.
Weather is notoriously difficult to predict. Complex systems are demanding of computational power. Further, the chaotic nature of, well, nature, makes accurate forecasting especially difficult the longer into the future one wants to look. Yet all is not lost!
In this interview, we explore the use of machine learning to help identify certain conditions under which the weather system has entered an unusually predictable position in it's normally chaotic state space.
Fler avsnitt av Data Skeptic
Visa alla avsnitt av Data SkepticData Skeptic med Kyle Polich finns tillgänglig på flera plattformar. Informationen på denna sida kommer från offentliga podd-flöden.