The Ice Sheet State and Parameter Estimator (ICESEE) Library (v1.0.0): Ensemble Kalman Filtering for Ice Sheet Models
Brian Kyanjo, Talea L. Mayo, and Alexander A. Robel

TL;DR
ICESEE is an open-source Python framework utilizing Ensemble Kalman Filtering for scalable, high-dimensional ice sheet data assimilation, enabling improved state estimation and parameter inference.
Contribution
It introduces a matrix-free, parallel EnKF implementation with multiple variants, supporting scalable, high-dimensional ice sheet model data assimilation.
Findings
Demonstrates ICESEE's interoperability with models like ISSM and Icepack.
Shows ICESEE's strong and weak scaling performance for large-scale applications.
Validates ICESEE's effectiveness in improving state estimates and inferring parameters.
Abstract
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.…
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