Revising the stochastic iterative ensemble smoother

Raanes, Patrick Nima; Stordal, Andreas Størksen; Evensen, Geir

doi:10.5194/npg-26-325-2019

Articles | Volume 26, issue 3

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Articles | Volume 26, issue 3

https://doi.org/10.5194/npg-26-325-2019

Articles | Volume 26, issue 3

Article
Peer review
Metrics
Related articles

Research article

17 Sep 2019

Research article |

| 17 Sep 2019

Revising the stochastic iterative ensemble smoother

Patrick Nima Raanes, Andreas Størksen Stordal, and Geir Evensen

Patrick Nima Raanes

CORRESPONDING AUTHOR

para@norceresearch.no

https://orcid.org/0000-0001-5073-6786

NORCE, Pb. 22 Nygårdstangen, 5838 Bergen, Norway

Nansen Environmental and Remote Sensing Center, Thormøhlens Gate 47, 5006 Bergen, Norway

Andreas Størksen Stordal

NORCE, Pb. 22 Nygårdstangen, 5838 Bergen, Norway

Geir Evensen

NORCE, Pb. 22 Nygårdstangen, 5838 Bergen, Norway

Nansen Environmental and Remote Sensing Center, Thormøhlens Gate 47, 5006 Bergen, Norway

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Loss-compensated snowfall scaling using large-area lidar observations improves snow depth simulations over multiple years

Jens Oprel, Tobias Jonas, Andreas S. Stordal, Felix N. Matt, Gaute Lappegard, Manuela I. Brunner, and Jan Magnusson

EGUsphere, https://doi.org/10.5194/egusphere-2026-5026,https://doi.org/10.5194/egusphere-2026-5026, 2026

This preprint is open for discussion and under review for The Cryosphere (TC).

Short summary

Snow models can be improved by adjusting snowfall inputs to match high-quality snow depth observations from airborne lidar scans. Existing methods cannot reliably adjust these inputs, especially in regions where melt occurred before the observations. By accounting for such losses, our new loss-compensated method provides a reliable way to integrate snow observations into models. Furthermore, this approach can also improve results in years without observations, using data from previous years.

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