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Just a reminder of our MET seminar in 1044 EOA given by Dr. Yair Cohen today! Snacks at 3 PM, talk at 3:15 PM.
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Allison Wing, Ph.D.</div>
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Associate Professor</div>
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Earth, Ocean and Atmospheric Science</div>
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Florida State University</div>
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<a href="mailto:awing@fsu.edu" class="">awing@fsu.edu</a></div>
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<div class="">On Oct 3, 2022, at 2:40 PM, eoas-seminar--- via Eoas-seminar <<a href="mailto:eoas-seminar@lists.fsu.edu" class="">eoas-seminar@lists.fsu.edu</a>> wrote:</div>
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Dear all,
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<div class="">Please join us on this <b class="">Thursday October 6</b> for our next Meteorology seminar, rescheduled from last week, given by <a href="https://climate-dynamics.org/people/ycohen/" class="">Dr. Yair Cohen</a>, a research scientist at the Climate
Modeling Alliance at Caltech. Dr. Cohen will speak about “<i class="">Combining physics and machine learning in the turbulence-convection parameterization of the CliMA climate model</i>”. </div>
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<div class="">Dr. Cohen will be joining us virtually <span class="" style="font-family: Helvetica, Arial;">but we will gather in EOA 1044 to participate in the seminar. If you cannot attend in person due to a medical reason or approved work out of town, please
contact Allison Wing (</span><a href="mailto:awing@fsu.edu" class="" style="font-family: Helvetica, Arial;">awing@fsu.edu</a><span class="" style="font-family: Helvetica, Arial;">) for remote access. Otherwise, we look forward to seeing everyone in 1044! Please
join us at 3 PM for refreshments prior to the beginning of the talk at 3:15 PM. </span></div>
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<div class=""><i class="" style="font-family: Helvetica, Arial;"><b class="">Graduate students are invited to participate in a student-only Q&A with the speaker at 2:15 PM in EOA 6067.</b></i><span class="" style="font-family: Helvetica, Arial;"> This is a
great opportunity to meet the speaker and discuss science and work/life/career topics in an informal setting. If you are interested in meeting individually with the speaker, please contact Allison Wing. </span></div>
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<div class="" style="font-family: Helvetica;"><b class="">DATE:</b> <b class="">Thursday October 6</b></div>
<div class="" style="font-family: Helvetica;"><b class="">STUDENT Q&A: </b>2:15 PM, EOA 6067</div>
<div class="" style="font-family: Helvetica;"><b class="">SEMINAR TIME:</b> Refreshments at 3 PM, Talk 3:15 PM - 4:15 PM. <br class="">
<b class="">SEMINAR LOCATION:</b> EOA 1044 (speaker remote)<br class="">
<b class="">SPEAKER:</b> <a href="https://climate-dynamics.org/people/ycohen/" class="">Dr. Yair Cohen</a> <br class="">
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<b class="">TITLE: </b>Combining physics and machine learning in the turbulence-convection parameterization of the CliMA climate model</div>
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<div class="" style="font-family: Helvetica;"><font face="Helvetica, Arial" class=""><b class="">ABSTRACT: </b></font><span class="" style="font-family: Roboto, RobotoDraft, Helvetica, Arial, sans-serif; white-space: pre-wrap; background-color: rgb(255, 255, 255);">The
representation of turbulence and convection at the subgrid scale of climate models by various parameterization schemes is one of the largest sources of model uncertainty in long term climate predictions. The crux of these parameterization schemes is the formulation
of closure functions for processes that cannot be observed or simulated in high resolution models. In this talk I will present a hybrid approach that combines a physics based parameterization scheme with physical and machine learning (ML) closures. Such a
hybrid approach cannot be trained using backpropagation typical to neutral networks, as this requires partial derivatives of the physics+ML setup in parameter space. Instead we use a gradient free (ensemble) method to train physics+ML setup from high resolution
LES data. The hybrid approach ensures conservation properties, allows for straightforward interpretability of its data driven components and reduces the degrees of freedom to allow us to train from spares data. The physical model is based on the extended Eddy
Diffusivity/Mass Flux (EDMF) scheme, derived by a systematic coarse-graining of the equations of motion and includes subgrid scale memory and prognostic equations for first and second moments. The closures for this scheme combine physical arguments with nondimensional
functional forms that can be learned from data using various ML architectures. Our parameterization, in a single column of a climate models, reproduces well the corresponding LES of the full spectrum of boundary layer and turbulent motions: from polar boundary
layers (vertical scale of 300m), through the stratocumulus-topped boundary layer (vertical scale of 1km), shallow convection (vertical scale of 3km), and deep convection (vertical scale of 15km). Furthermore, using ML closures we show that a hybrid model is
able to extrapolate by training on current climate simulations to a validation set from 4K climate warming scenario.</span></div>
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Allison Wing, Ph.D.</div>
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Associate Professor</div>
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Earth, Ocean and Atmospheric Science</div>
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Florida State University</div>
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<a href="mailto:awing@fsu.edu" class="">awing@fsu.edu</a></div>
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