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Welcome to Marijn van der Meer, who joins us as a scientific assistant! 😊 Marijn will be working with Harry Zekollari and focusing on emulating #glacier dynamics using machine learning. 💻 Happy to have you with us, Marijn!

New article by Sophie de Roda Husman where she intercompares different satellites for assessing surface melt 🌊 over the entire Antarctic Ice Sheet 🇦🇶. ieeexplore.ieee.org/document/99283…

Happy International Women's Day! 🥳 To celebrate, we made a short film to highlight the women of our group and the research that they do.

Group member Marijn van der Meer just returned from the 'Machine Learning in Glaciology' workshop in Norway, where she was able to collaborate with other machine learning experts and enjoy these gorgeous views! 🤩❄️☀️ Universitetet i Oslo Universitetet i Bergen Utrecht University TU Delft UC Berkeley University of Twente (inactive)

[1/4] We’ve been brainstorming in response to the lack of #diversity in the #EGU23 Division Awards. We see potential for #action at both the top #EGU level and the level of individual Divisions.

🤹‍♀️Our true polymath, Marijn van der Meer, wrote a fabulous paper on her MSc thesis work👩‍🎓. Together with Stef Lhermitte and me, she developed an emulator to reproduce regional high-resolution 🔎 surface mass balance predictions over the Antarctic Peninsula 🇦🇶from a GCM.

📢 Impressive work by team member Marijn van der Meer! In her first published paper in AGU (American Geophysical Union)'s #JAMES, she demonstrates the power of fast machine learning emulators 🤖 to downscale global climate models to regional resolution. Congratulations! 🎉 Link below ⬇️ agupubs.onlinelibrary.wiley.com/doi/full/10.10…

🚨 New RSE paper by Sophie de Roda Husman where we present UMelt: an Antarctic-wide 🇦🇶, high-resolution surface melt record by merging multi-source remote sensing data 🛰️ using deep learning 🤖. doi.org/10.1016/j.rse.…

Discover UMelt🕵️‍♀️, an Antarctic-wide surface melt product with impressive high resolution (500 meters, 12 hours). Dive into the data, exploring both daily and seasonal melt patterns on this Google Earth Engine app: …sophiederoda.projects.earthengine.app/view/umelt-ins…. (1/6)

Group member Marijn van der Meer was a co-author on this paper from Sophie de Roda Husman looking at surface melt in Antarctica using deep-learning! 🇦🇶💻 They made an instructive and easy-to-use product to examine daily and seasonal melt. Check it out! 🤓

Really interesting presentations and discussions yesterday during the "Data science in Glaciology" symposium TU Delft. Thanks everyone for coming, and looking forward to new collaborations and ideas!

Last week, PhD student Marijn van der Meer spoke about her research at this seminar on data science in glaciology! She presented her current work modeling glacier mass balance using machine learning. A great opportunity to get in touch with other interdisciplinary scientists 🙂

We are excited to announce the 3rd edition of the “Machine Learning in Glaciology” workshop, in the Finse alpine research station (Norway) 🇳🇴 The workshop will take place on 6-12 September 2024, and the deadline to apply is April 8! ❄️ …ning-in-glaciology-workshop.github.io

🧑‍🎓4 year PhD Opportunity on Antarctic Climate Causality KU Leuven and UCLouvain under supervision of Nicole van Lipzig, Hugues Goosse and me More details and application form: kuleuven.be/personeel/jobs… 🗓️ Deadline 29 April 2024

🚨 New paper! 🚨 Meet SUPREME💫: a method designed to downscale Antarctic surface melt from a regional climate model using a physically-informed super-resolution architecture. (1/5)

Preprint alert! #VAW PhD student Marijn van der Meer introduces miniML-MB, a machine-learning model for glacier mass balance. 🏔️ It outperforms simple PDD models in most cases—exciting news for future ML applications in glacier modeling! 💻 Check it out: egusphere.copernicus.org/preprints/2024…