[Eoas-seminar] [Seminar-announce] Scientific Computing Colloquium with Junhong Liang

[eoas-seminar at lists.fsu.edu]

"Data-driven parameterization of ocean surface boundary layer mixing"

Junhong Liang
Department of Earth, Ocean and Atmospheric Science (EOAS)
Florida State University


Please feel free to forward/share this invitation with other groups/disciplines that might be interested in this talk/topic. All are welcome to attend.

NOTE: In-person attendance is requested in our 499 Dirac Science Library (DSL) Seminar Room. Zoom access is intended for external (non-departmental) participants.


https://fsu.zoom.us/j/94273595552
Meeting # 942 7359 5552

🎦 Colloquium recordings will be made available here, https://www.sc.fsu.edu/colloquium<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.sc.fsu.edu%2Fcolloquium&data=05%7C02%7Csc-seminar-announce%40lists.fsu.edu%7C56a5010c155b4559226808de9357d491%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C639110202584314027%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=eiZMl%2BkxPtHgds6bFsCzwMKjkZ7FLkizkb9DIpGzHeQ%3D&reserved=0>


Wednesday, Apr 8, 2026, Schedule:

* 3:00 to 3:30 PM Eastern Time (US and Canada)
☕ Nespresso & Teatime - 417 DSL Commons

* 3:30 to 4:30 PM Eastern Time (US and Canada)
🕟 Colloquium - 499 DSL Seminar Room


Abstract:
In ocean and climate models, turbulence is not explicitly resolved and must be parameterized. The simulation of upper-ocean physical states, therefore, depends critically on mixing parameterizations for ocean surface boundary layer turbulence. Existing schemes are formulated with algebraic formulas based on physical principles with a few tunable empirical parameters. They cannot fully represent the diversity of conditions in the real ocean, contributing to biases in ocean and climate simulations. In this talk, I will present our recent efforts to develop data-driven parameterizations of turbulent mixing for use in ocean models. In our approach, neural networks are trained on a large ensemble of large-eddy simulations that explicitly compute upper-ocean turbulence. The resulting data-driven parameterizations are more accurate than traditional schemes, while remaining efficient and numerically stable. We have implemented this approach in the Hybrid Coordinate Ocean Model (HYCOM) and demonstrated improved simulations of upper-ocean states in the Gulf of Mexico.


Additional colloquium details can be found here.
https://www.sc.fsu.edu/news-and-events/colloquium/1922-colloquium-with-junhong-liang-2026-04-08<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.sc.fsu.edu%2Fnews-and-events%2Fcolloquium%2F1922-colloquium-with-junhong-liang-2026-04-08&data=05%7C02%7Csc-seminar-announce%40lists.fsu.edu%7C56a5010c155b4559226808de9357d491%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C639110202584340834%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=%2F0qS4%2B73ZcRr287EJ8%2FPkB8n7sXunsRrRl4U1UQPZ80%3D&reserved=0>
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