Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 18
Articles | Volume 19, issue 18
https://doi.org/10.5194/gmd-19-8565-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-8565-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 18
Development and technical paper
 | 
16 Sep 2026
Development and technical paper |  | 16 Sep 2026

Evolving beyond collapse: an adaptive particle batch smoother for cryospheric data assimilation

Kristoffer Aalstad, Esteban Alonso-González, Norbert Pirk, Sebastian Westermann, Clarissa Willmes, and Ruitang Yang

Supplement

https://doi.org/10.5194/gmd-19-8565-2026-supplement

Data sets

Code and data to reproduce the results and figures Esteban Alonso-González and Kristoffer Aalstad https://doi.org/10.5281/zenodo.21244337

Inputs (forcing and observations) ready for use by 'MuSA: The Multiscale Snow Data Assimilation System (v1.0)' Esteban Alonso-González https://doi.org/10.5281/zenodo.7248635

ESM-SnowMIP meteorological and evaluation datasets at ten reference sites (in situ and bias corrected reanalysis data) Cecile Menard and Richard Essery https://doi.org/10.1594/PANGAEA.897575

Model code and software

MuSA: The Multiple Snow Assimilation system Authors/Creators Esteban Alonso-González and Kristoffer Aalstad https://doi.org/10.5281/zenodo.17292981

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Short summary
AdaPBS (Adaptive Particle Batch Smoother) is a new algorithm to combine observations with cryospheric numerical models. AdaPBS is an iterative algorithm that automatically adjusts computing effort to the task, allowing the implementation of early stopping strategies. We tested AdaPBS at multiple sites with different models, matching or outperforming standard methods, when compared against more complex (computationally expensive) algorithms.
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