[Eoas-seminar] MET Seminar - Tuesday September 22 - 3 PM - Dr. Emily De Jong (Lawrence Livermore National Lab)

[eoas-seminar at lists.fsu.edu]

Dear all,

Please join us for the second Meteorology seminar of Fall 2026 this coming Tuesday, September 22, at 3 PM, given by Dr. Emily De Jong from Lawrence Livermore National Laboratory. She will speak about “Bridging Scales in Cloud Microphysics from Observation to Simulation” (abstract below).

Dr. De Jong will be joining us remotely, but we will meet in person to watch the seminar together in EOA 1044. A Zoom link is available for those with a medical excuse or approved work off-campus. Please contact Michael Diamond (msdiamond at fsu.edu) for the link.

DATE: Tuesday September 22
TIME: 3-4 PM, please join early for refreshments
LOCATION: EOA 1044
SPEAKER: Dr. Emily De Jong (https://ekdejong.github.io/<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fekdejong.github.io%2F&data=05%7C02%7Ceoas-seminar%40lists.fsu.edu%7C1d0f25c2b31a49c22e6008df13fbb786%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C639251643981183586%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=A90gKUvFQ4mOKOHFn0OJjJYh6o3l0%2FLeL3a%2BBNi9b28%3D&reserved=0>)

We look forward to seeing you there!

Best,
Michael on behalf of the MET seminar committee (Allison, Chelsea, & Michael)

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Bridging Scales in Cloud Microphysics from Observation to Simulation

Cloud microphysics—the processes governing droplets, ice, and aerosols at microscopic scales—remains a leading source of uncertainty in weather and climate predictions. These processes shape cloud structure, precipitation, and radiative feedbacks, yet they are neither resolvable in large-scale models nor directly constrained by most observing systems. Bridging the scale gap between observations, microphysical processes, and predictive models is a central challenge in atmospheric science.

This talk presents a data-driven approach to integrating satellite observations, high-fidelity simulations, and subgrid-scale modeling to better constrain and represent cloud microphysics across scales. First, I will present recent work that applies machine learning to infer vertically resolved cloud properties from passive satellite observations, combining multi-modal data sources to reconstruct properties and structure that are not directly observed. This approach provides new observation-informed constraints across regimes and helps bridge the gap between sparse microphysical measurements and global passive observations. Next, I will introduce a complementary framework for learning reduced-order representations of cloud microphysics from high-fidelity particle-based simulations. This surrogate model discovers compact, physically-consistent, and performant representations of cloud droplet dynamics, enabling efficient and interpretable representations of warm cloud processes in weather and climate models. Together, these approaches illustrate a pathway for integrating observations and simulations within a unified data-driven framework. By linking space-based observation to vertically-resolved cloud properties and reduced-order parameterization to droplet-scale details, this work aims to improve the physical realism and predictive capability of atmosphere prediction systems.

--
Dr. Michael S. Diamond
Assistant Professor of Meteorology
https://michael-s-diamond.github.io/camp/<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmichael-s-diamond.github.io%2Fcamp%2F&data=05%7C02%7Ceoas-seminar%40lists.fsu.edu%7C1d0f25c2b31a49c22e6008df13fbb786%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C639251643981240470%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=yoUFGPjicuRr0b0E9keud0B4hQ97afLZvld3nZ7U4Pk%3D&reserved=0>
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