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Hi all,</div>
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Abstract submission for the 2025 IAGA / IASPEI Joint Scientific Meeting (<span style="color: rgb(70, 120, 134);"><u><a href="https://iaga-iaspei-2025.org/" id="OWAe851ca8e-5041-871d-0a4a-8d4b1f62c005" class="OWAAutoLink" style="color: rgb(70, 120, 134);">https://iaga-iaspei-2025.org/</a></u></span>).
The meeting will be held in Lisbon, Portugal, August 31st – September 5th, 2025. The meeting aims to bring together researchers of geomagnetism, aeronomy, and internal geodynamics. One of the many interesting sessions this year is led by the IAGA/URSI Geospace
Data Assimilation Working Group (GeoDAWG, https://sites.google.com/view/geodawg/) and we encourage anyone interested in data assimilation across geospace to submit abstracts to the session ahead of the March 12th deadline.</div>
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<b>J04 Data assimilation and Machine Learning: Challenges and Leveraging New Opportunities</b></div>
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<b>Convener: Laure Lefevre (Belgium)</b></div>
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Co-convener(s): David Themens (UK), Tomoko Matsuo (USA), Kyle Gwirtz (USA), Jacob Bortnik (USA), Sacha Lapins (UK)</div>
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Data assimilation is a powerful statistical learning framework that combines models, observations, and their respective uncertainties, allowing us to unify data-driven scientific induction with first principle-based deductions. The framework in the general
form can be applied to any geophysical system, with many of the challenges faced broadly spanning disciplines. On the other hand, applications of data assimilation and statistical learning techniques to sparsely observed geophysical systems (such as the core,
mantle, cryosphere, hydrosphere, thermosphere and ionosphere, and magnetosphere) face considerable challenges, requiring innovative adaptation of methods to maximize the use of sparse observations, and considerable research efforts to quantify model and observational
uncertainties. Recently, advances in Machine Learning have presented new opportunities to leverage more complex observations and develop probabilistic or reduced order parametrizations of complex physical processes and entire models. These techniques present
new opportunities to explore coupled or computationally prohibitive data assimilation approaches that otherwise would not be tractable. This session invites submissions broadly on the topic of data assimilation and the implementation of machine learning techniques
to facilitate geospatial environment specification and discovery. Submissions should span any application of data assimilation or machine learning in IAGA or IASPEI domains.</div>
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Looking forward to seeing many of you in Lisbon!</div>
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Cheers,</div>
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David R. Themens</div>
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