Marijn
@marijnvandermeer.bsky.social
Doctoral student in glaciology @ ETH Zürich. Specialised in machine learning applied to climate science and the cryosphere.
(2) Tested on independent Norwegian glaciers, MBM was compared with GloGEM, OGGM & PyGEM:
✅ RMSE ≈ 0.59–1.00 m w.e.
✅ Bias: –0.01 to +0.04 m w.e.
✅ Outperforms conventional models for seasonal MB
✅ Promising transferability across glaciers & climates 🌍
✅ RMSE ≈ 0.59–1.00 m w.e.
✅ Bias: –0.01 to +0.04 m w.e.
✅ Outperforms conventional models for seasonal MB
✅ Promising transferability across glaciers & climates 🌍
November 17, 2025 at 9:11 AM
(2) Tested on independent Norwegian glaciers, MBM was compared with GloGEM, OGGM & PyGEM:
✅ RMSE ≈ 0.59–1.00 m w.e.
✅ Bias: –0.01 to +0.04 m w.e.
✅ Outperforms conventional models for seasonal MB
✅ Promising transferability across glaciers & climates 🌍
✅ RMSE ≈ 0.59–1.00 m w.e.
✅ Bias: –0.01 to +0.04 m w.e.
✅ Outperforms conventional models for seasonal MB
✅ Promising transferability across glaciers & climates 🌍
🧊 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.
(3/3) Using just two predictors obtained through dimensionality reduction techniques, miniML-MB can closely match the PMB for individual glacier sites, surpassing the PDD model for most sites as long as predictions are made within a range of meteorological conditions similar to the training set.
February 24, 2025 at 8:11 AM
(3/3) Using just two predictors obtained through dimensionality reduction techniques, miniML-MB can closely match the PMB for individual glacier sites, surpassing the PDD model for most sites as long as predictions are made within a range of meteorological conditions similar to the training set.
(2/3) Our dimensionality reduction framework singles out summer temps (May–Aug) & winter precip (Oct–Feb) as key drivers of glacier PMB. Unlike PDD models, our ML approach directly selects predictors from data—boosting performance & validating climatic drivers in Swiss glaciers.
February 24, 2025 at 8:08 AM
(2/3) Our dimensionality reduction framework singles out summer temps (May–Aug) & winter precip (Oct–Feb) as key drivers of glacier PMB. Unlike PDD models, our ML approach directly selects predictors from data—boosting performance & validating climatic drivers in Swiss glaciers.
(1/3) miniML-MB is designed to simulate annual point mass balance at individual glacier sites using meteorological variables (air temperature and total precipitation). We rely on data collected at 28 individual measurement sites across the Swiss Alps:
February 24, 2025 at 8:05 AM
(1/3) miniML-MB is designed to simulate annual point mass balance at individual glacier sites using meteorological variables (air temperature and total precipitation). We rely on data collected at 28 individual measurement sites across the Swiss Alps: