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<p class="MsoNormal">Dear Colleagues,<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Abstract submission is open for AGU 2024, and we would like to draw your attention to our session
<b>SA014</b> on data assimilation and data science “Leverage state-of-the-art models with observational data to improve the simulations and predictions from the ground to geospace”.<o:p></o:p></p>
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<p class="MsoNormal">Looking forward to seeing you at AGU.<o:p></o:p></p>
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<p class="MsoNormal">Best Regards,<o:p></o:p></p>
<p class="MsoNormal">Guiping Liu, Fabrizio Sassi, Alexa Halford, Hanli Liu, Robert Redmon<o:p></o:p></p>
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<p class="MsoNormal">--<o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:Roboto;color:black;background:white;mso-ligatures:none">Whole atmosphere models from the ground to geospace have been widely used to interpret and understand the complex variability of the atmosphere,
ionosphere, and Magnetosphere (AIM). However, the modeled results are often found deviating from observations to a point that the physics-based models using traditional data assimilation (DA) methods are not able to fully represent AIM variations on various
spatial and temporal scales. Meanwhile, existing observations are not adequate and can be subject to aliasing due to limited sampling and coverage. To mitigate this, a novel concept of data-model integration needs to be developed, one that combines DA with
new advanced data science approaches and technologies in artificial intelligence/machine learning (AI/ML) in AIM theory and interpretation.</span><span style="font-size:12.0pt;mso-ligatures:none"><o:p></o:p></span></p>
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<span style="font-size:12.0pt;font-family:Roboto;color:black;mso-ligatures:none">This session calls for presentations on DA and AI/ML, and new observations and measurement techniques are also welcome. The goal is to encourage strategies to better leverage state-of-the-art
models with observations for AIM research.<o:p></o:p></span></p>
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