[Eoas-seminar] [Seminar-announce] Scientific Computing Colloquium with Jeffrey Whitaker

[eoas-seminar at lists.fsu.edu]

"Assimilating real observations with ML-based models"

Jeffrey Whitaker
Meteorologist, Modeling and Data Assimilation,
National Oceanic and Atmospheric Administration (NOAA)


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 only.

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%7Cc893d83d1e694cf4969d08dcfa990d0e%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C638660782450747640%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=UMJHXtvdHm%2Boczx5NqckfzRXSqDuEIq2Gsck7GcrXqQ%3D&reserved=0>

Wednesday, Nov 6th, 2024, 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:
There has been a recent surge in development of accurate global atmospheric model emulators, including full ML model emulators (FourCastNet, PanguWeather, GraphCast, GenCast) and hybrid models (NeuralGCM). Evaluation of these models has generally been focused on forecasts, mostly with 1-10 day lead times. However, forecasts rely on accurate initial conditions that, in the context of operational weather prediction, generally come from a cycled data assimilation (DA) system. While there have been a few DA studies that utilize emulators, these studies have focused on synthetic observation experiments. Here we present what is, to our knowledge, the first study of a DA cycling system using ML models to assimilate real observations. A reduced observation network consisting of only surface pressure observations is assimilated into ML models (including NeuralGCM, PanguWeather, and GraphCast) using an ensemble Kalman filter. This experiment design provides an informative test case since the performance of the DA system relies on accurately capturing ensemble covariances to update unobserved state variables, where the observed variables may or may not be part of the loss function used in the optimization process. Results will be compared to those from experiments that cycle with a coarse-resolution version of a fully physical model, NOAA’s operational Global Forecast System. The ML models suffer from stability issues, and even when stable do not perform well compared to the GFS, even when run at higher resolution, suggesting that changes in the way these models are trained will be necessary for DA applications.


Additional colloquium details can be found here,
https://www.sc.fsu.edu/news-and-events/colloquium/1824-colloquium-with-jeffrey-whitaker-2024-11-06<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.sc.fsu.edu%2Fnews-and-events%2Fcolloquium%2F1824-colloquium-with-jeffrey-whitaker-2024-11-06&data=05%7C02%7Csc-seminar-announce%40lists.fsu.edu%7Cc893d83d1e694cf4969d08dcfa990d0e%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C638660782450903843%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=L%2BZhu7mgA1Z27INJaXAGM8liWZizod3hog32JZHPpGk%3D&reserved=0>
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