PhD Candidate

University of Colorado - Boulder

About Me

Tim Higgins is a PhD Candidate in the Climate Processes and Predictability group at the University of Colorado - Boulder Department of Atmospheric and Oceanic Sciences. His research focuses on understanding predictability in our climate system and improving prediction of atmospheric rivers across various scales. Much of his work leverages machine learning and generative artificial intelligence to make progress in achieving these goals.

Interests
  • Atmospheric Rivers
  • Generative AI
  • Weather and Climate Prediction
Education

  • PhD Atmospheric and Oceanic Sciences

    University of Colorado - Boulder

  • BSc Atmospheric Sciences and Meteorology, Mathematics (Applied)

    University of Miami

Featured Publications
Higgins, T. B., A. C. Subramanian, P. A. G. Watson, and S. Sparrow. (2025). Changes to Atmospheric River Related Extremes over the United States West Coast under Anthropogenic Warming. GRL.
Cite Source Document
Higgins, T. B., A. C. Subramanian, W. E. Chapman, D. A. Lavers, and A. C. Winters. (2024). Subseasonal Potential Predictability of Horizontal Water Vapor Transport and Precipitation Extremes in the North Pacific. WAF.
Cite Source Document DOI
Higgins, T. B., A. C. Subramanian, A. Graubner, L. KappSchwoerer, P. A. G. Watson, S. Sparrow, K. Kashinath, S. Kim, L. Delle Monache, and W. Chapman. (2023). Using Deep Learning for an Analysis of Atmospheric Rivers in a High-Resolution Large Ensemble Climate Dataset. JAMES.
Cite Source Document DOI