<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class="">Dear Colleagues,<br class=""><br class="">You are invited to submit an abstract to the upcoming <b class="">TESS session #3 "Improving Understanding of the Sun-Earth System Through Advanced Statistical and Machine Learning Techniques”</b>. The session description is attached below.<br class=""><br class="">The Triennial Earth-Sun Summit (TESS) is a joint meeting of the AAS Solar Physics Division and the Space Physics and Aeronomy Section of the AGU (<a href="https://aas.org/meetings/tess2022" class="">https://aas.org/meetings/tess2022</a>). The meeting will take place in Bellevue, Seattle, August 8-12, 2022. <b class="">The abstract deadline is Friday, April 15, 2022.</b><br class=""><br class=""><p class=""><font class=""><u class="">Topical Session #3:</u> Improving Understanding of the Sun-Earth System Through Advanced Statistical and Machine Learning Techniques</font></p><p class=""><font class=""><u class="">Conveners:</u> Enrico Camporeale, Tuija Pulkkinen, </font>Shasha Zou</p><div class=""><font class=""><span class="c1"></span></font></div><p class="c0"><font class=""><span class="c1">The increasing availability of big data in solar-terrestrial (ST) sciences and the rapidly growing computational capability have enabled numerous successful applications of the advanced statistical and machine learning techniques to the ST sciences. In particular, they are critical in improving and increasing the lead time of space weather predictions. </span>This session will focus on progress in applying advanced statistical and machine learning techniques to a broad range of processes in the Sun-Earth system. We solicit papers addressing topics such as forecasting solar magnetism and solar eruption, forecasting of solar energetic particles, and understanding the impact of the solar eruptions on the geospace system using ML techniques. Contributions ranging from black-box models to physics-informed and interpretable machine learning methods as well as their coupling with physics-based models are all welcome. We especially seek contributions that demonstrate how the new methods enable scientific discovery, deepen our scientific understanding, or advance operational space weather forecasting.</font></p><div class="">
<div dir="auto" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); letter-spacing: normal; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration: none; word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div>----------------------------------------------------------------------------------------------<br class="">Shasha Zou<br class="">Associate Professor<br class="">Department of Climate and Space Sciences and Engineering (CLaSP)<br class="">University of Michigan<br class="">1431 Space Research Building, 2455 Hayward St.<br class="">Ann Arbor, MI 48109-2143<br class="">(office) 734-936-8184<br class=""><a href="http://zou.engin.umich.edu" class="">http://zou.engin.umich.edu</a></div><div>----------------------------------------------------------------------------------------------<br class=""><br class=""></div><div class=""><br class=""></div></div><br class="Apple-interchange-newline"><br class="Apple-interchange-newline">
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