Marijn
@marijnvandermeer.bsky.social
Doctoral student in glaciology @ ETH Zürich. Specialised in machine learning applied to climate science and the cryosphere.
Pinned
Machine learning improves seasonal mass balance prediction for unmonitored glaciers
Abstract. Glacier evolution models based on temperature-index approaches are commonly used to assess hydrological impacts of glacier changes. However, current model calibration frameworks cannot effic...
doi.org
🚨 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
@vaw-glaciology.bsky.social
🚨 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
@vaw-glaciology.bsky.social
Machine learning improves seasonal mass balance prediction for unmonitored glaciers
Abstract. Glacier evolution models based on temperature-index approaches are commonly used to assess hydrological impacts of glacier changes. However, current model calibration frameworks cannot effic...
doi.org
November 17, 2025 at 9:10 AM
🚨 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
@vaw-glaciology.bsky.social
Reposted by Marijn
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.
September 3, 2025 at 7:30 AM
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.
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.
April 1, 2025 at 7:45 AM
🧊 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.
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/...
tc.copernicus.org/articles/19/...
A minimal machine-learning glacier mass balance model
Abstract. Glacier retreat presents significant environmental and social challenges. Understanding the local impacts of climatic drivers on glacier evolution is crucial, with mass balance being a centr...
tc.copernicus.org
February 24, 2025 at 8:02 AM
🚨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/...
tc.copernicus.org/articles/19/...
Reposted by Marijn
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! 💻🇳🇴
February 12, 2025 at 4:39 PM
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 :) 🏔️
January 30, 2025 at 12:44 PM
🚨 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 :) 🏔️