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<div style="margin: 0px; font-stretch: normal; line-height: normal;" class=""><font face="Helvetica Neue" size="2" class="">We invite you to submit an abstract to the Fall 2022 AGU Session SM026: Solar Wind Uncertainty and its Impact on Geospace Physics, Modeling,
and Data Analysis. The session aims to focus on variability and uncertainties in the solar wind, assumptions about the solar wind drivers, and how these impact our models and understanding of the geospace system. We wish to provide a platform for studies investigating
the formulation and propagation of solar wind uncertainties and their effects on the magnetosphere-ionosphere response that use but are not limited to data analysis, machine learning-based feature importance studies, bias and variance analysis, ensemble modeling,
and numerical experiments. Studies that explore measurement or model uncertainties for extreme space weather events are also encouraged.<br class="">
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Abstracts are due 3rd August 2022. AGU 2022 will be held in Chicago and Online over December 12 - 16, 2022.<br class="">
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For more info about the session please use this link: <a href="https://agu.confex.com/agu/fm22/prelim.cgi/Session/161886" class="">https://agu.confex.com/agu/fm22/prelim.cgi/Session/161886</a></font><span style="font-family: "Helvetica Neue"; font-size: small;" class="">.</span></div>
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If you have questions, please email Nithin Sivadas at <a href="mailto:sivadas@cua.edu" class="">sivadas@cua.edu</a></font><span style="font-family: "Helvetica Neue"; font-size: small;" class="">.</span><span style="font-family: "Helvetica Neue"; font-size: small;" class=""> </span></div>
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Please note that this session is part of the SPA-SM section, but it is also cross-listed with SPA-SA and SPA-SH. Hence one can submit a contributed abstract to this session even if another contributed abstract is submitted to another SPA-SM or other sessions.<br class="">
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Session Conveners: Nithin Sivadas (NASA GSFC/CUA), Dogacan Su Ozturk (UAF), Brian Walsh (BU), Homayon Aryan (UCLA), Mei-Yun Lin (UIUC)</font></div>
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