A Kalman Filter-based Framework for Assimilating Remote Sensing Observations into a Surface Mass Balance Model

Herrmann O, Groos AR, Tabone I, Jouvet G, Fürst J (2025)

Publication Type: Journal article

Publication year: 2025

Journal

DOI: 10.1017/aog.2025.10020

Abstract

This study introduces a custom implementation of the Ensemble Kalman Filter (EnKF) for calibrating a 3D glacier evolution model. The EnKF can assimilate observations as they become available and provides uncertainty measures for the initial state after calibration. We calibrate an elevationdependent surface mass balance model using elevation change observations and test the EnKF s performance in a Twin Experiment by varying internal and external hyperparameters. The best-performing configuration is applied to the Rh ne Glacier in a Real-World experiment. Using satellite-based elevation change fields for calibration, the EnKF estimates an average equilibrium line altitude of 2920 37m for the period 20002019. A comparison of the results with glaciological measurements demonstrates the capabilities of the EnKF to simultaneously calibrate multiple surface mass balance parameters. With this proof of concept, we expect that our methodology is readily extendable to other map or point observations and their combination, as well as to other calibration parameters.

Authors with CRIS profile

Involved external institutions

Université de Lausanne (UNIL) Switzerland (CH)

How to cite

APA:

Herrmann, O., Groos, A.R., Tabone, I., Jouvet, G., & Fürst, J. (2025). A Kalman Filter-based Framework for Assimilating Remote Sensing Observations into a Surface Mass Balance Model. Annals of Glaciology. https://doi.org/10.1017/aog.2025.10020

MLA:

Herrmann, Oskar, et al. "A Kalman Filter-based Framework for Assimilating Remote Sensing Observations into a Surface Mass Balance Model." Annals of Glaciology (2025).

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