Seeing the Atmosphere through Machine Learning
Machine learning algorithms are part of our everyday digital lives, but did you know that they can also be used in atmospheric research? These algorithms can improve weather predictions, assist forecasters in identifying hazards, and aid in increasing our understanding of atmospheric phenomena. Join NSF NCAR scientist David John Gagne as he provides an overview of machine learning algorithms commonly used in atmospheric science research, such as in producing more accurate predictions of hailstorms and hurricanes. Peer inside the black box of machine learning as it discovers the patterns that lead to severe weather, and learn about the challenges, blind spots, and potential hazards of machine learning.
David John Gagne
David John Gagne is a Machine Learning Scientist in the Computational Information Systems Laboratory (CISL) and the Research Applications Laboratory (RAL) at NSF NCAR. His research focuses on developing machine learning systems to improve the prediction and understanding of high impact weather, and to enhance weather and climate models. During his time at NSF NCAR, he has collaborated with interdisciplinary teams to produce machine learning systems to study hail, tornadoes, hurricanes, and renewable energy. He has also developed short courses and hackathons to provide atmospheric scientists hands-on experience with machine learning. Gagne received his Ph.D. in meteorology from the University of Oklahoma in 2016 and completed an Advanced Study Program postdoctoral fellowship at NSF NCAR in 2018. In addition to his duties at NSF NCAR, he also serves as chair of the American Meteorological Society Artificial Intelligence Committee.