Articles | Volume 19, issue 11
https://doi.org/10.5194/tc-19-5423-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-5423-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Nov 2025
Research article |
| 06 Nov 2025
| 06 Nov 2025
Estimation of the state and parameters in ice sheet model using an ensemble Kalman filter and Observing System Simulation Experiments
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Alek Petty
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Denis Felikson
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Jonathan Poterjoy
Department of Atmospheric & Oceanic Science, University of Maryland, College Park, MD, USA
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Short summary
We combined numerical models with satellite observations using the ensemble Kalman filter to improve predictions of glacier states and their basal conditions. Our simulations show that incorporating more data generally improves prediction accuracy. We also tested different types of data and found that the high-resolution observations provide the greatest improvements. This method can help guide the design of future observing systems and improve long-term projections of ice sheet change.
We combined numerical models with satellite observations using the ensemble Kalman filter to...
