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.

翻译：ICESEE（冰盖状态与参数估计器）是一个基于Python的开源数据同化框架，旨在与冰盖及地球系统模型实现无缝集成。该框架实现了并行化的集合卡尔曼滤波（EnKF），并在状态和参数空间中通过完全MPI支持实现可扩展的同化功能。ICESEE采用Evensen（2003）提出的无矩阵更新方案，避免了显式预报误差协方差构建，消除了在高维非线性系统中进行局地化的需求。该框架同时支持四种EnKF变体（包括用于方法测试的局地化版本），可通过混合同化-反演策略间接推断未观测模型参数。系统具备模块化耦合接口、自适应状态索引及高效并行I/O功能，可扩展至多种建模环境。ICESEE已成功与ISSM、Icepack等模型实现耦合。本研究聚焦于其在ISSM和Icepack中的应用，展示了ICESEE的互操作性、性能表现、可扩展性以及提升状态估计精度和推断不确定参数的能力。性能基准测试展示了强扩展性与弱扩展性，凸显了ICESEE在大规模观测约束冰盖再分析中的应用潜力。