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| Main Authors: | , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.26947 |
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| _version_ | 1866912985651347456 |
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| author | Kyanjo, Brian Mayo, Talea L. Robel, Alexander A. |
| author_facet | Kyanjo, Brian Mayo, Talea L. Robel, Alexander A. |
| contents | ICESEE (ICE Sheet statE and parameter Estimator) is a Python-based, open-source data assimilation framework designed for seamless integration with ice sheet and Earth system models. It implements a parallel Ensemble Kalman Filter (EnKF) with full MPI support for scalable assimilation in state and parameter spaces. ICESEE uses a matrix-free update scheme from Evensen (2003), which avoids explicit forecast error covariance construction and eliminates the need for localization in high-dimensional, nonlinear systems. ICESEE also supports four EnKF variants, including a localized version for methodological testing. It enables indirect inference of unobserved model parameters through a hybrid assimilation-inversion strategy. The framework features modular coupling interfaces, adaptive state indexing, and efficient parallel I/O, making it extensible to a variety of modeling environments. ICESEE has been successfully coupled with ISSM, Icepack, and other models. In this study, we focus on applications with ISSM and Icepack, demonstrating ICESEE's interoperability, performance, scalability, and ability to improve state estimates and infer uncertain parameters. Performance benchmarks show strong and weak scaling, highlighting ICESEE's potential for large-scale, observation-constrained ice sheet reanalyses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26947 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Ice Sheet State and Parameter Estimator (ICESEE) Library (v1.0.0): Ensemble Kalman Filtering for Ice Sheet Models Kyanjo, Brian Mayo, Talea L. Robel, Alexander A. Computational Complexity ICESEE (ICE Sheet statE and parameter Estimator) is a Python-based, open-source data assimilation framework designed for seamless integration with ice sheet and Earth system models. It implements a parallel Ensemble Kalman Filter (EnKF) with full MPI support for scalable assimilation in state and parameter spaces. ICESEE uses a matrix-free update scheme from Evensen (2003), which avoids explicit forecast error covariance construction and eliminates the need for localization in high-dimensional, nonlinear systems. ICESEE also supports four EnKF variants, including a localized version for methodological testing. It enables indirect inference of unobserved model parameters through a hybrid assimilation-inversion strategy. The framework features modular coupling interfaces, adaptive state indexing, and efficient parallel I/O, making it extensible to a variety of modeling environments. ICESEE has been successfully coupled with ISSM, Icepack, and other models. In this study, we focus on applications with ISSM and Icepack, demonstrating ICESEE's interoperability, performance, scalability, and ability to improve state estimates and infer uncertain parameters. Performance benchmarks show strong and weak scaling, highlighting ICESEE's potential for large-scale, observation-constrained ice sheet reanalyses. |
| title | The Ice Sheet State and Parameter Estimator (ICESEE) Library (v1.0.0): Ensemble Kalman Filtering for Ice Sheet Models |
| topic | Computational Complexity |
| url | https://arxiv.org/abs/2603.26947 |