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<span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">OAPS Short Seminar Series</span>
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11:00 AM May 5th</div>
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Attend F2F (in 255 Research A) or Virtually (via Zoom)</div>
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https://fsu.zoom.us/j/92268262553</div>
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Meeting ID: 922 6826 2553</div>
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Talks are normally 12 minutes long with an additional 8 minutes for questions.</div>
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Identifying and predicting the Lagrangian coherence of eddies in the Gulf of Mexico using machine learning and satellite observations</div>
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By Luna Hiron</div>
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Description: Lagrangian coherent eddies efficiently transport water properties, such as heat and salt, as well as tracers, including oil, larvae, and Sargassum, throughout the ocean. For instance, during the 2010 Deepwater Horizon oil spill, part of the oil
was captured within a Loop Current Frontal Eddy (LCFE), preventing it from reaching the Florida Keys. Similarly, Loop Current Eddies (LCEs) carry warmer, saltier waters typical of the Caribbean Sea to the western Gulf of Mexico (GoM). In this study, we employ
machine learning alongside various satellite observations–absolute dynamic topography (ADT), sea surface temperature (SST), and chlorophyll-a (Chl-a)–to identify Lagrangian coherent eddies in the GoM and predict their lifetime. Three durations of Lagrangian
coherence are investigated: 5, 10, and 20 days. This study also investigates the contributions of Chl-a to identifying and forecasting LCEs' and LCFEs' Lagrangian coherence, aiming to assess the advantages of integrating this dataset into data-assimilative
Gulf ocean models, in addition to ADT and SST. The machine learning model trained with ADT successfully identifies and predicts the lifetimes of eddies, achieving accuracy rates of 90% for LCE identification and 93% for lifetime prediction, along with 71%
and 61% for LCFEs, respectively. Incorporating SST and Chl-a enhanced eddy predictions over ADT-only or ADT and SST combined, in particular LCEs and LCFEs, highlighting the benefits of assimilating Chl-a into ocean models to improve the representation and
the forecast of these eddies. This machine learning framework has the potential to advance predictions of eddy lifetimes and the advection of various tracers.</div>
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Weighted essentially non-oscillatory tracer advection scheme in MOM6</div>
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By Yao Gahounzo</div>
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Description: This talk will focus on implementing and evaluating high-order Weighted Essentially Non-Oscillatory (WENO) schemes for the tracer advection in the Modular Ocean Model version 6 (MOM6). WENO schemes are known for achieving high-order accuracy while
effectively suppressing spurious oscillations near sharp gradients. Compared to the existing piecewise parabolic advection scheme in MOM6, the WENO method offers improved accuracy. Numerical results demonstrate that the WENO scheme reduces numerical dissipation
and maintains accuracy without producing non-physical oscillations.</div>
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NOTE: 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.</div>
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