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<p class="MsoNormal"><b><span style="font-size:10.0pt;font-family:"Verdana",sans-serif;color:black">Planning to attend the American Geophysical Union (AGU) Fall Meeting?<o:p></o:p></span></b></p>
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<p class="MsoNormal"><b><span style="font-size:10.0pt;font-family:"Verdana",sans-serif;color:black">This session might interest you or someone you know.<o:p></o:p></span></b></p>
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<p class="MsoNormal"><b><span style="font-size:10.0pt;font-family:"Verdana",sans-serif;color:black">Abstracts Due:
</span></b><span style="font-size:10.0pt;font-family:"Verdana",sans-serif;color:black">4 August 2021 23:59 EDT/3:59 +1 GMT<o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:10.0pt;font-family:"Verdana",sans-serif;color:black">Session ID:</span></b><span style="font-size:10.0pt;font-family:"Verdana",sans-serif;color:black"> 123315 <br>
<b>Session Title:</b> IN031. Process-based modeling and AI/ML for Predicting Global Environmental Change <br>
<b>Section:</b> Earth and Space Science Informatics <br>
<b>Virtual Only Session (selected by primary convener during session submission): </b>No <br>
<b>View Session Details:</b></span> <a href="https://agu.confex.com/agu/fm21/prelim.cgi/Session/123315">
https://agu.confex.com/agu/fm21/prelim.cgi/Session/123315</a> <o:p></o:p></p>
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<p class="MsoNormal"><b><span style="font-size:10.0pt;font-family:"Verdana",sans-serif;color:black">Session Summary:</span></b><span style="font-size:10.5pt;font-family:Roboto;color:#262626"> We are experiencing catastrophic global environmental change: global
warming, population growth, and the sixth mass extinction. There is a pressing need to understand why our world is changing and predict changes to adapt. Despite progress made through emerging data analytics technologies (e.g., artificial intelligence - AI
and machine learning - ML), there remains a need to understand causal effects of interconnected human-natural systems. <b><span style="border:none windowtext 1.0pt;padding:0in">AI/ML </span></b>alone cannot capture the feedbacks and processes connecting land,
water, atmosphere, and biosphere. For this, we rely on process-based models (e.g., radiative transfer, hydrology, fire behavior) but they often prohibit operational uses. We invite presentations on novel applications of <b><span style="border:none windowtext 1.0pt;padding:0in">AI/ML </span></b>and
process-based models to provide near-real-time predictive capabilities. We encourage submissions to describe environmental processes and discuss the use of <b><span style="border:none windowtext 1.0pt;padding:0in">AI/ML </span></b>for data collection, integration,
inference, or computational efficiency. Methods can include: synthetic data, emulation, scale-aware, reduced order, imputation, and data fusion.</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#202124">--<o:p></o:p></span></p>
<p class="MsoNormal"><a href="https://wkidsolutions.com/resume/" target="_blank"><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#0563C1">E. Natasha Stavros</span></a><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#202124;background:white">,
PhD (She/Her)</span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#202124">Director of <a href="https://www.colorado.edu/earthlab/analytics-hub" target="_blank"><span style="color:#0563C1">Earth Lab Analytics Hub</span></a><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#202124">Cooperative Institute of Research for Environmental Science (CIRES)<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#202124">University of Colorado Boulder<o:p></o:p></span></p>
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