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

Viewed

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 1,587 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,365 159 63 1,587 64 70 55
  • HTML: 1,365
  • PDF: 159
  • XML: 63
  • Total: 1,587
  • Supplement: 64
  • BibTeX: 70
  • EndNote: 55
Views and downloads (calculated since 23 Apr 2026)
Cumulative views and downloads (calculated since 23 Apr 2026)

Viewed (geographical distribution)

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 1,587 (including HTML, PDF, and XML) Thereof 1,582 with geography defined and 5 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 27 Oct 2026
Download
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.
Read more
Share
Altmetrics
Final-revised paper
Preprint