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.

03.09.2025 07:30 — 👍 16    🔁 2    💬 0    📌 0

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, in large-scale applications, these models lack ca...

Link to preprint: egusphere.copernicus.org/preprints/20...

01.04.2025 07:45 — 👍 1    🔁 0    💬 0    📌 0

🧊 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.

01.04.2025 07:45 — 👍 2    🔁 0    💬 1    📌 0

(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.

24.02.2025 08:11 — 👍 1    🔁 0    💬 1    📌 0

(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.

24.02.2025 08:08 — 👍 1    🔁 0    💬 1    📌 0

(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:

24.02.2025 08:05 — 👍 0    🔁 0    💬 1    📌 0

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...

🚨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/...

24.02.2025 08:02 — 👍 3    🔁 1    💬 1    📌 1

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! 💻🇳🇴

12.02.2025 16:39 — 👍 15    🔁 2    💬 1    📌 1

GitHub - ODINN-SciML/MassBalanceMachine: Global machine learning glacier mass balance model, capable of assimilating all sources of glaciological and remote sensing data Global machine learning glacier mass balance model, capable of assimilating all sources of glaciological and remote sensing data - ODINN-SciML/MassBalanceMachine

In case of interest in the Mass Balance Machine, check out our GitHub 🧊 github.com/ODINN-SciML/...

12.02.2025 16:40 — 👍 1    🔁 0    💬 0    📌 0

LinkedIn This link will take you to a page that’s not on LinkedIn

Link to apply: sirop.org/app/75ab995e...

30.01.2025 12:44 — 👍 1    🔁 0    💬 0    📌 0

🚨 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 :) 🏔️

30.01.2025 12:44 — 👍 2    🔁 0    💬 1    📌 0