ICESEE
ICESEE (ICE ShEet State and Parameter Estimator) is a data assimilation software framework designed for coupling with ice-sheet, climate, and geophysical models such as ISSM, Icepack, and idealized models such as Lorenz-96. It provides a modular and extensible platform for ensemble forecasting, state estimation, parameter estimation, and uncertainty quantification across a wide range of scientific modeling applications.
What is ICESEE?
ICESEE (ICE ShEet state and parameter Estimator) is a scalable ensemble-based data assimilation framework designed for coupling with ice-sheet, climate, and geophysical models such as ISSM, Icepack, and idealized systems like Lorenz-96.
ICESEE provides a modular and extensible scientific computing infrastructure for ensemble forecasting, uncertainty quantification, parameter estimation, and hybrid physics–AI workflows in large-scale environmental modeling.
The framework supports:
- Ensemble-based data assimilation methods
- Distributed-memory HPC execution
- Cloud-enabled scientific workflows
- Cross-language model coupling
- AI-enhanced scientific computing and machine learning integration
Getting Started
To get started with ICESEE:
- Installation Guide
- Using ICESEE
- Build ICESEE as a Package
- Developmental Notes
Related Projects
| Project | Description |
|---|---|
| ICESEE-Spack | HPC and cluster deployment |
| ICESEE-Containers | Containerized execution environments |
| ICESEE-GHUB | Cloud and browser-based scientific workflows |
For Cluster installation and runs, see ICESEE-Spack or ICESEE-Containers and for cloud runs, see ICESEE-GHUB
ICESEE CI Lorenz96 smoke test
The figure shown below is generated directly from the automated test workflow of Lorenz96 and serves as a lightweight end-to-end verification of the framework.
Figure: Automatically generated by the ICESEE CI workflow after executing the Lorenz-96 assimilation benchmark. The figure compares the true trajectory, background forecast, synthetic observations, and ensemble analysis produced by ICESEE.
AI/ML Integration
ICESEE is being extended with scientific machine learning and AI-enhanced workflows to support next-generation hybrid physics–AI modeling and scalable intelligent simulation systems.
Current and planned AI/ML capabilities include:
- Machine learning–based parameter estimation
- Neural-network observation operators
- AI-enhanced ensemble initialization
- Adaptive covariance and inflation tuning
- Surrogate forward models for accelerated simulations
- Intelligent workflow automation and HPC optimization
- AI-assisted diagnostics and parameter tuning
The AI/ML framework is designed to integrate seamlessly with existing ensemble-based data assimilation workflows while maintaining compatibility with distributed HPC environments and scientific modeling systems.
Supported Models
- icepack: PDE-based ice-sheet modeling with Firedrake
- issm: Finite-element ice-sheet modeling via MATLAB coupling
- lorenz96: Idealized nonlinear data assimilation benchmark
- flowline_model: Simple ice-flow simulation
Documentation
Explore the Wiki to find:
- Configuration and setup tips
- Model implementation guides
- Filter development and customization
- Data assimilation workflows
- Debugging and troubleshooting guides
- HPC deployment recommendations
Key Features
- Modular ensemble-based data assimilation framework
- State and parameter estimation capabilities
- Cross-language scientific model coupling
- Scalable HPC and distributed-memory workflows
- HDF5/NetCDF-based scientific data pipelines
- Containerized and cloud-enabled execution
- Extensible APIs for integrating external models
- Support for uncertainty quantification and ensemble forecasting
- Reproducible scientific computing infrastructure
Key Features
- Modular ensemble-based data assimilation framework
- Cross-language scientific model coupling
- Scalable HPC and distributed-memory workflows
- HDF5/NetCDF-based scientific data pipelines
- Containerized and cloud-enabled execution
- AI/ML-ready scientific workflow infrastructure
- Extensible APIs for integrating external models
- Support for uncertainty quantification and ensemble forecasting
Future Directions
- Integration of scientific machine learning workflows into data assimilation pipelines
- Development of neural surrogate models for accelerated ice-sheet simulations
- AI-enhanced parameter estimation and adaptive ensemble tuning
- Integration with AWS and cloud-native scientific computing environments
- Expansion of distributed HPC workflows for large-scale ensemble forecasting
- Incorporation into the GHUB online ice-sheet platform
-
Intelligent workflow orchestration and automated HPC diagnostics
- Machine learning–based parameter estimation
- Neural-network observation operators
- AI-enhanced ensemble initialization
- Adaptive covariance and inflation tuning
- Surrogate forward models for accelerated simulations
- Intelligent workflow automation and HPC optimization
- AI-assisted diagnostics and parameter tuning
These capabilities are under active development and are not yet part of the stable ICESEE release.
Future Directions
- Integration of scientific machine learning workflows into data assimilation pipelines
- Development of neural surrogate models for accelerated ice-sheet simulations
- AI-assisted parameter estimation and adaptive ensemble tuning
- Integration with cloud-native scientific computing environments
- Expansion of distributed HPC workflows for large-scale ensemble forecasting
- Continued development of the ICESEE-GHUB ecosystem
- Intelligent workflow orchestration and automated diagnostics
Contributing
Contributions are welcome. Please open an issue or submit a pull request through the GitHub repository.
For questions, suggestions, or collaboration opportunities:
Brian Kyanjo
bkyanjo3@gatech.edu
License
ICESEE is distributed as free and open-source software under the BSD 2-Clause License (see LICENSE).
External software packages and coupled models, including ISSM, Icepack, Firedrake, MATLAB-based components, and other third-party dependencies, remain subject to their respective licenses, which are independent of the ICESEE license.