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<span style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);">Dear all, </span>
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<div><font color="#000000">Please join us next </font><b style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);">Thursday April 11</b><font color="#000000"> for a Meteorology seminar given by </font><a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsshamekh.com%2Fhome%2F&data=05%7C02%7Ceoas-seminar%40lists.fsu.edu%7C7029c725955844adb92408dc5570783a%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C638479188734616875%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=kAP10a7%2BIagFfZKx9KwR99LhdbLH8gr21DQteOdfpag%3D&reserved=0" originalsrc="https://sshamekh.com/home/" shash="q6kl9SHKsD8kONf6IGze8OJJZHJt75DzpM39NChdD4PR5xFNALLBbJsCqGOZkmmnEApGkoyqF8y64v3K1wnFYI79lrZ7HTWbOxlktRfTFZTtLnlwkKBPAcSNnILuCR+Ue46bXzL/kn0YI7g7COsr3LAN4ztneIVBMwExSaaljuU=">Prof.
Sara Shamekh</a><font color="#000000"> from NYU. </font><font color="#000000">she will speak about “T</font><i style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);">oward a better representation of atmospheric processes using machine learning</i><i style="color: rgb(0, 0, 0);">”
</i><span style="color: rgb(0, 0, 0);">(abstract below)</span></div>
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<div><font color="#000000">Prof. Shamekh will be joining us virtually, but we will still gather together in EOA 1044. If you have a medical excuse or other approved work off-campus, please contact Allison Wing (<a href="mailto:awing@fsu.edu">awing@fsu.edu</a>)
for the Zoom link. Otherwise we look forward to seeing everyone in 1044. </font></div>
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<div><font color="#000000">Prof. Shamekh is also available for individual Zoom meetings on Thursday after the seminar. If you’d like to meet with her, please contact Allison Wing. </font></div>
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<div><b>DATE: </b>Thursday April 11</div>
<div><b>SEMINAR TIME: </b>Refreshments at 3 PM, Talk 3:15 - 4:15 PM</div>
<div><b>SEMINAR LOCATION: </b>EOA 1044 (Speaker remote)</div>
<div><b>SPEAKER: </b><a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsshamekh.com%2Fhome%2F&data=05%7C02%7Ceoas-seminar%40lists.fsu.edu%7C7029c725955844adb92408dc5570783a%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C638479188734616875%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=kAP10a7%2BIagFfZKx9KwR99LhdbLH8gr21DQteOdfpag%3D&reserved=0" originalsrc="https://sshamekh.com/home/" shash="q6kl9SHKsD8kONf6IGze8OJJZHJt75DzpM39NChdD4PR5xFNALLBbJsCqGOZkmmnEApGkoyqF8y64v3K1wnFYI79lrZ7HTWbOxlktRfTFZTtLnlwkKBPAcSNnILuCR+Ue46bXzL/kn0YI7g7COsr3LAN4ztneIVBMwExSaaljuU=" style="caret-color: rgb(255, 255, 255);">Prof.
Sara Shamekh</a></div>
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<b style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);">TITLE:</b><font color="#000000"> <span style="caret-color: rgb(0, 0, 0);"><i>Toward a better representation of atmospheric processes using machine learning</i></span></font>
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<b>ABSTRACT:</b> As the impact of climate change poses an urgent concern for the future of our planet and all its inhabitants, it's crucial that we accurately understand and model this complex system. By doing so, we can develop the ability to take action towards
adapting to the impact of climate change. Climate models are important tools for understanding and predicting global and regional climate change, yet they exhibit key uncertainties that limit their applicability to future projections. Uncertainties in climate
models partly originate from a poor or lacking representation of physical processes too small to be resolved by models, such as atmospheric boundary layer turbulence or clouds. Machine learning has the ability to capture nonlinear structures and relationships
within complex data and, when combined with traditional physical models, can lead to a better representation of physical processes and provide new insights into atmospheric processes. In this talk, I will discuss few examples that highlight the potential of
machine learning (ML) combined with physics and the new discoveries made possible through this framework. The first example uses reduced-order models to accurately represent vertical turbulent fluxes in the atmospheric boundary layer across turbulent regimes.
The architecture of this model, in which I enforce a physical constraint, allows clear interpretability and discovery of the main modes of turbulent transport. I then compare this model with an ML-enhanced eddy-diffusivity-mass-flux parameterization (a typical
conventional parameterization of atmospheric boundary layer) to demonstrate how machine-discovered modes of variability may differ from the conventional ones. I then discuss how to incorporate the spatial pattern of a field into a parameterization in order
to address some of their limiting assumptions. These examples show the promise of ML in advancing our understanding and modeling of physical processes in the earth system. Despite these successes, many challenges remain to be addressed and many questions to
be answered, making the future of this interdisciplinary area exciting.</div>
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<div style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);">We look forward to seeing you there!</div>
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<div style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);">Cheers, </div>
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<div style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);">Allison</div>
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Allison A. Wing, Ph.D.</div>
<div>Werner A. and Shirley B. Baum Professor<br>
Associate Professor, Department of Earth, Ocean, and Atmospheric Science<br>
Florida State University<br>
awing@fsu.edu</div>
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