🚨 Introducing the Mass Balance Machine (MBM): a #data-driven glacier MB model using topographic + climate features 🏔️ 👉 "Machine learning improves seasonal mass balance prediction for unmonitored glaciers” (Sjursen et al., 2025) doi.org/10.5194/tc-1... @vaw-glaciology.bsky.social

Greetings from Marijn van der Meer at the #Ellis Summer School in Jena 🇩🇪! She presented her work on the #MassBalanceMachine. The school brings together AI & climate science & is co-organised by top institutes & supported by @climatechangeai.bsky.social, @esa.int Academy & more.

🧊 New preprint: We present the Mass Balance Machine (MBM), an XGBoost-based model predicting glacier mass balance at high resolution, even for glaciers without in situ data. Applied to Norwegian glaciers, MBM generalizes well, outperforming TI models in seasonal mass balance prediction.

🚨Introducing miniML-MB: a #MachineLearning model using XGBoost to estimate glacier mass balance from very small datasets! Applied in the Swiss Alps, it pinpoints key drivers—May–Aug temp & Oct–Feb precip—and outperforms a basic PDD model. @vaw-glaciology.bsky.social tc.copernicus.org/articles/19/...

Greetings from sunny, winter-cold Oslo and the Global Glacier Modelling Workshop 2025! ❄️ Several #VAW members joined to present exciting new developments on the Global Glacier Evolution Model as well as the Mass Balance Machine. Three productive days of great discussions! 💻🇳🇴

🚨 MSc thesis opportunity at @vaw-glaciology.bsky.social The thesis focuses on applying a glacier mass balance model based on machine learning, driven by climate variables and topographical features. This is a great opportunity to apply data science to a real-world problem :) 🏔️