An ETKF approach for initial state and parameter estimation in ice sheet modelling
International audience 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 answe...
Published in: | Nonlinear Processes in Geophysics |
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Main Authors: | , , , |
Other Authors: | , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
HAL CCSD
2014
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Subjects: | |
Online Access: | https://inria.hal.science/hal-01066882 https://inria.hal.science/hal-01066882/document https://inria.hal.science/hal-01066882/file/npg-21-569-2014.pdf https://doi.org/10.5194/npg-21-569-2014 |
Summary: | International audience 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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