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Dear all,
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<div>This is a reminder of today’s MET seminar <font color="#000000">given by </font><a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmariajmolina.github.io%2F&data=05%7C02%7Ceoas-seminar%40lists.fsu.edu%7Cbbc3bfc1210146bfd0b308dc651f3ef4%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C638496432066390615%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=13SH5y3FYzan%2FhxKAdZ8wDS0xTY9gT14tVCBINSJpmY%3D&reserved=0" originalsrc="https://mariajmolina.github.io/" shash="c3n/ZR3d5YZhx21JeE9S+AvE8i4XNrGbnP5gRhvYawLxPkRLn8B22IIiB169jEsPMAYFZWer75ivzwLBdmIe03z9YmMrmlIj34e/RZZMjRa3QUdY7zTFlrSsb07ooLhyjWf6gBvhxWmk52eE2f1SqmAkUFoaKWLLggJ9P2x9OCY=" originalsrc="https://mariajmolina.github.io/" shash="nNvwaEFPIS5INNVIw+FPmX4nZ53NH7R+RFfvarelCs3eFHSSwKysL1Jmt0ozLygZniz3IUWqQki8hzYusf+Sj2kLY0oz0yH7F/jkHxdiPnfsyNkz+amy0nUgo32iQ7LrWQn8eaPFOnp4EpBbdOU1fA6wFZukTllzggB/jd5ZI5s=">Prof.
Maria Molina</a><font color="#000000"> of the University of Maryland. S</font><font color="#000000">he will speak about <i>“</i></font><i>Machine learning for Earth system prediction and predictability</i>”<font color="#000000"><span style="caret-color: rgb(0, 0, 0);"><i>. </i></span></font></div>
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<div><font color="#000000"><span style="caret-color: rgb(0, 0, 0);"><b>Snacks at 3, talk at 3:15 in 1044.</b></span></font></div>
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<div><font color="#000000"><span style="caret-color: rgb(0, 0, 0);">See you there!</span></font></div>
<div><font color="#000000"><span style="caret-color: rgb(0, 0, 0);"><br>
</span></font></div>
<div><font color="#000000"><span style="caret-color: rgb(0, 0, 0);">Cheers, </span></font></div>
<div><font color="#000000"><span style="caret-color: rgb(0, 0, 0);"><br>
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<div><font color="#000000"><span style="caret-color: rgb(0, 0, 0);">Allison</span></font></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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<blockquote type="cite">
<div>On Apr 19, 2024, at 8:11 AM, eoas-seminar--- via Eoas-seminar <eoas-seminar@lists.fsu.edu> wrote:</div>
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<span style="caret-color: rgb(0, 0, 0);">Dear all, </span>
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<div><font>Please join us next </font><b style="caret-color: rgb(0, 0, 0);">Thursday April 25</b><font> for a Meteorology seminar given by </font><a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmariajmolina.github.io%2F&data=05%7C02%7Ceoas-seminar%40lists.fsu.edu%7Cbbc3bfc1210146bfd0b308dc651f3ef4%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C638496432070453371%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=dAk9I0EzZoi2dDTF14pnP7La9cHdn%2B%2FgvZvNSaUnEgs%3D&reserved=0" originalsrc="https://mariajmolina.github.io/" shash="QoTJwJe6f2mxqgFyN5JYX5D+jKUNcR1PSoXEjbmbTbLV/jsCr3iRUFqCEkmcLdlOwwMQostk/CpZ7F6JchETHyu7eWSf7h6Q28iArJ0V7gFvDyZYOJhBRjdMYY2ou1WohzRMiUIkcZOoBIX61170cdn5S7eYiKCEFZqUZEQ2nmE=" originalsrc="https://mariajmolina.github.io/" shash="nNvwaEFPIS5INNVIw+FPmX4nZ53NH7R+RFfvarelCs3eFHSSwKysL1Jmt0ozLygZniz3IUWqQki8hzYusf+Sj2kLY0oz0yH7F/jkHxdiPnfsyNkz+amy0nUgo32iQ7LrWQn8eaPFOnp4EpBbdOU1fA6wFZukTllzggB/jd5ZI5s=">Prof.
Maria Molina</a><font> of the University of Maryland. S</font><font>he will speak about
<i>“</i></font><i>Machine learning for Earth system prediction and predictability</i>"<i style=""> </i><span style="">(abstract below). </span></div>
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<div><font>Prof. Molina will be joining us in person. </font><span style="">She is also available for individual meetings on Thursday. If you’d like to meet with her, please contact Allison Wing (<a href="mailto:awing@fsu.edu">awing@fsu.edu</a>). </span><span style="caret-color: rgb(0, 0, 0);">As
some of you may recall, Prof. Molina received her Bachelors degree in Meteorology from FSU in 2008, so she is particularly excited to catch up with some of her former professors. </span></div>
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<div><span style="caret-color: rgb(0, 0, 0);">Graduate students, please join for lunch with the speaker at 12:15 PM on Thursday. Please RSVP to Allison Wing by the end of the day Tuesday. </span></div>
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<div><b>DATE: </b>Thursday April 25</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 in person)</div>
<div><b>SPEAKER:</b><a href="https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmariajmolina.github.io%2F&data=05%7C02%7Ceoas-seminar%40lists.fsu.edu%7Cbbc3bfc1210146bfd0b308dc651f3ef4%7Ca36450ebdb0642a78d1b026719f701e3%7C0%7C0%7C638496432070453371%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=dAk9I0EzZoi2dDTF14pnP7La9cHdn%2B%2FgvZvNSaUnEgs%3D&reserved=0" originalsrc="https://mariajmolina.github.io/" shash="QoTJwJe6f2mxqgFyN5JYX5D+jKUNcR1PSoXEjbmbTbLV/jsCr3iRUFqCEkmcLdlOwwMQostk/CpZ7F6JchETHyu7eWSf7h6Q28iArJ0V7gFvDyZYOJhBRjdMYY2ou1WohzRMiUIkcZOoBIX61170cdn5S7eYiKCEFZqUZEQ2nmE=" originalsrc="https://mariajmolina.github.io/" shash="rBQYW9xkRTufA0/Nm5o3WNMlvpm01nBSTCjh4h5kE4ugF9lMUACm1JGhPZimsoKMY6jlXmRabcaULvRWY87edRsfNWWXNMyr1AF4PHROpCaK7NrX/ybMq3fCNybYhmZuayFjFqM5xFUR4oOBZOcWkOuXF4TZH4ahSN1j6ZlzKBs=" style="caret-color: rgb(255, 255, 255);">Prof.
Maria Molina</a></div>
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<div style="caret-color: rgb(0, 0, 0);"><br>
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<b style="caret-color: rgb(0, 0, 0);">TITLE:</b><font> </font><i>Machine learning for Earth system prediction and predictability</i>
<div style="caret-color: rgb(0, 0, 0);"><br>
<b>ABSTRACT:</b> Machine learning can be used for Earth system prediction, or to study our ability to make skillful predictions given the system's initial state or other factors, otherwise known as predictability. In traditional numerical weather prediction
frameworks, we solve the governing partial differential equations starting from an initial state. This initialized prediction framework usually involves three stages: 1) generating the initial conditions of the Earth system, 2) running the mathematical representation
of the system on a computer forward in time, and 3) analyzing the output and converting it into a format that is useful for end users. Machine learning can be used to improve each of these individual stages, or to circumvent the three stage framework altogether,
and examples of each will be given in this seminar. More time during the seminar will be dedicated to the challenges surrounding subseasonal prediction, which focuses on lead times of three to four weeks, and how we can use machine learning to both uncover
potential biases in our initialized prediction systems and how we can bias-correct them.</div>
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<div style="caret-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);">Cheers, </div>
<div style="caret-color: rgb(0, 0, 0);"><br>
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<div style="caret-color: rgb(0, 0, 0);">Allison</div>
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<div>--------------------------------------------<br>
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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