An ETKF approach for initial state and parameter estimation in ice sheet modelling
Estimating the contribution of Antarctica and Greenland to sea-level rise is a hot topic in glaciology. Good estimates rely on our ability to run a precisely calibrated ice sheet evolution model starting from a reliable initial state. Data assimilation aims to provide an answer to this problem by co...
| Published in: | Nonlinear Processes in Geophysics |
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| Main Authors: | , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Copernicus Publications
2014
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/npg-21-569-2014 https://noa.gwlb.de/receive/cop_mods_00020094 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00020049/npg-21-569-2014.pdf https://npg.copernicus.org/articles/21/569/2014/npg-21-569-2014.pdf |
| Summary: | Estimating the contribution of Antarctica and Greenland to sea-level rise is a hot topic in glaciology. Good estimates rely on our ability to run a precisely calibrated ice sheet evolution model starting from a reliable initial state. Data assimilation aims to provide an answer to this problem by combining the model equations with observations. In this paper we aim to study a state-of-the-art ensemble Kalman filter (ETKF) to address this problem. This method is implemented and validated in the twin experiments framework for a shallow ice flowline model of ice dynamics. The results are very encouraging, as they show a good convergence of the ETKF (with localisation and inflation), even for small-sized ensembles. |
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