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</o:shapelayout></xml><![endif]--><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:black">Dear Colleagues</span><span style="color:black"><o:p></o:p></span>
<div class="WordSection1">
<p class="MsoNormal"><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:black"> </span><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:black">We invite you to submit to
our<span class="apple-converted-space"> </span>AGU<span class="apple-converted-space"> </span>session on <b>“</b><span style="background:white">App</span>l<span style="background:white">ications of </span>Machine<span style="background:white"> </span>L<span style="background:white">earning A</span>l<span style="background:white">gorithms in Mode</span>l<span style="background:white">ing Atmospheric Aeroso</span>l<span style="background:white">s, C</span>l<span style="background:white">ouds and Radiation</span><b>”</b>.
Our motivation for this session is to foster talks and
discussions that can enhance predictive understanding of
Earth System Models by applications of novel machine
learning algorithms and tools with a focus on improving the
accuracy and speed of model predictions that are constrained
by atmospheric physics and chemistry. The deadline for
abstract submission is Wednesday July 29th, 2020.</span><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:black"> </span><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:black">Please share this information
with those you think might also be interested.</span><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:black"> </span><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:black">All the best,</span><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:black">The Session Co-Conveners</span><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:black">Manish Shrivastava, Anthony
Wexler, Ziheng Sun, Daniel Tong</span><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:black"> </span><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:#262626">Session Title:</span></b><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:black;background:white">App</span><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:black">l<span style="background:white">ications of </span>Machine<span style="background:white"> </span>L<span style="background:white">earning A</span>l<span style="background:white">gorithms in Mode</span>l<span style="background:white">ing Atmospheric Aeroso</span>l<span style="background:white">s, C</span>l<span style="background:white">ouds and Radiation</span></span><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:#262626"> </span></b><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:#262626">Session Description:</span></b><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:#262626;background:white">Recent
breakthroughs in machine learning algorithms and artificial
intelligence provide unprecedented tools for enhancing
predictive understanding of the existing Earth System
Models. These tools could be used for a wide range of
problems, such as replacing computationally expensive
modules, representing stochastic processes in radiative
transfer models, using physics informed neural networks to
understand unknown processes, and representing and
propagating uncertainties in atmospheric models across
different spatial and temporal scales. However, progress in
scientific applications has been comparatively slow. It is
challenging to generalize the ML models for a larger scale,
interpret the trained models, and improve the
reproducibility. We invite talks on innovative machine
learning algorithms focused on modeling atmospheric
aerosols, clouds and radiation. These could range from
replacing existing computationally expensive modules to
reducing model error, interpreting physical processes and
uncertainty quantification, and integrating AI models into
the established numeric models to achieve better quality and
lower cost.</span><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:black"> </span><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:#262626">More info:</span><span style="color:black"><o:p></o:p></span></p>
<p class="MsoNormal"><strong><span style="font-size:14.0pt;font-family:"Calibri",sans-serif;color:black">Session
ID:</span></strong><span class="apple-converted-space"><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:black;background:white"> </span></span><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:black;background:white">105898<span class="apple-converted-space"> </span></span><span style="font-size:14.0pt;font-family:"Times New
Roman",serif;color:black"><br>
<strong>Session Title:</strong><span class="apple-converted-space"> </span>. Applications of<span class="apple-converted-space"> </span>Machine<span class="apple-converted-space"> </span>Learning Algorithms
in Modeling Atmospheric Aerosols, Clouds and Radiation<span class="apple-converted-space"> </span><br>
<strong>Section:</strong><span class="apple-converted-space"><span style="background:white"> </span></span><span style="background:white">Atmospheric Sciences<span class="apple-converted-space"> </span></span><br>
<strong>View Session Details:</strong><span class="apple-converted-space"><span style="background:white"> </span></span><a href="https://secure-web.cisco.com/1g6gVXaBC-aYVDnmRQ3DDmzcOm-gAlOL4QyBlGEHca257ED7k5rB4o9Npdx2d-jWoT9MiW5u9EOjtFPNY3-6IfRuzbgVxXxsiMUotHFZL7TW3ANPJmi08XM-1RBz43hbIeiGqyLrvTukj2oL4mazSohw75-BfCjVl5loJORPQWlKJGlEygGfoTL45SRhsWtUlBk1GmMMyGPwBZ5w9NSLMOqkb1PdPpuLWU2lHsAp5Xq-jsO4HqlI5mWesbZcJGxYkRFiygRztLUvHMV7sQ726jF1S0p5y0UU8Q8CzcOK3w_rv9-rcVBYA3sFZ0JKt-5ADyvmy71ZShcZCrwRh1--dXQgDNNb9SFXo8YKCxeH_uXYcmgF4uIOvTKYgBh4LVX5dOMFqukCMCduGMhCJ8aF5cF2Qd95j50VS1J2pTyv7py95xSi5JNvzOqHM5vGsAjPw/https%3A%2F%2Fagu.confex.com%2Fagu%2Ffm20%2Fprelim.cgi%2FSession%2F105898" title="https://agu.confex.com/agu/fm20/prelim.cgi/Session/105898" moz-do-not-send="true">https://agu.confex.com/agu/fm20/prelim.cgi/Session/105898</a></span><span style="color:black"><o:p></o:p></span></p>
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