Timothy Smith
Integrated Data Assimilation and Artificial Intelligence RA
- Ph.D. in Computational Science, Engineering, and Mathematics; The University of Texas at Austin; December 2021
- M.S. in Computational Science, Engineering, and Mathematics; The University of Texas at Austin; May 2017
- B.S. in Mechanical Engineering; The University of Texas at Austin; May 2014
Research Interests
Before joining PSL, I obtained my Ph.D. in Computational Science, Engineering, and Mathematics from the Oden Institute at UT Austin. My graduate work focused on quantifying uncertainties that are inherent to ocean models, and I implemented a generic, adjoint-based framework to propagate these uncertainties onto predictions from the MIT general circulation model. I used this framework to show how sparse observations of the ocean state reduce uncertainty in simulation-based estimates of ocean driven melting underneath the Pine Island ice shelf, which is fed by one of the fastest flowing glaciers in Antarctica. The results showed how valuable observations in this region are for constraining modeled quantities.
Current Research
I am interested in advancing coupled data assimilation techniques for the next generation of weather forecasting systems. My main focus is in developing Machine Learning methods that can enable strongly coupled data assimilation, so that observations of the atmosphere can impact estimates of the ocean state, and vice versa, directly within the data assimilation framework.
Research Categories
Atmosphere, Climate and Weather, Oceansto
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About CECA
CECA connects and creates a supportive environment for graduate students and postdocs who come from various academic units to do research in CIRES.