ICESEE on GHUB

ICESEE-GHUB is an interactive Jupyter Book environment that brings the ICESEE (Ice-sheet Coupled Ensemble Simulator and Estimator) framework to the cloud through GHUB. This tool enables users to explore ensemble data assimilation workflows, run lightweight examples, and understand the ICESEE framework without requiring a full HPC setup.

What is ICESEE?

ICESEE supports ensemble data assimilation workflows (e.g., EnKF-style methods) with an emphasis on:

• Modular structure — Reuse the same DA logic across different models
• Model coupling — Integrate external codes/workflows while keeping the assimilation engine consistent
• Scalability — Execute on HPC and cloud-style environments, including GHUB

For detailed implementation and broader documentation, see the ICESEE Wiki.

✨ Key Features

• Interactive Jupyter Book format with comprehensive documentation
• Runnable tutorials including the Lorenz-96 data assimilation demo
• Cloud-ready deployment through GHUB infrastructure
• Ensemble data assimilation workflows (EnKF-style methods)
• Modular design for coupling with ice-sheet models (ISSM, Icepack, flowline solvers)
• No HPC required for lightweight examples

Prerequisites

• Access to GHUB (https://theghub.org)
• For local development:
  • Python 3.8+
  • Anaconda or Miniconda
  • Jupyter Book

Quick Start

On GHUB

1. Navigate to the ICESEE tool on GHUB
2. Launch the tool
3. The Jupyter Book will open automatically
4. Start with the Lorenz-96 tutorial for a complete end-to-end example

Local Installation

# Clone the repository
git clone  https://github.com/ICESEE-project/ICESEE-GHUB.git
cd ICESEE-GHUB

# Get dependencies and the kernal installed and activated
./tools/go.icesee1 && source ./tools/create_icesee1_environment_yml.sh

Documentation

This repository provides comprehensive documentation through a Jupyter Book, including:

• Quickstart Guide — Fastest way to get started
• User Manual — Practical usage notes
• ICESEE Workflow — Conceptual overview of the DA cycle
• Tutorial Notebooks — Interactive examples including:
  • Lorenz-96 data assimilation demo (runnable in GHUB)
  • Flowline model coupling
  • ISSM and Icepack integration examples
• HPC Coupling Guide — Advanced deployment patterns
• Container Usage — Docker/Singularity workflows

For upstream ICESEE documentation: ICESEE Wiki

Project Structure

ICESEE-GHUB/
├── icesee_jupyter_book/     # Jupyter Book source files
│   ├── icesee_jupyter_notebooks/  # Tutorial notebooks
│   ├── _config.yml          # Book configuration
│   ├── _toc.yml            # Table of contents
│   └── *.md                # Documentation pages
├── external/
│   └── ICESEE/             # ICESEE core (git subtree)
├── bin/                    # Scripts for launching the book
├── middleware/             # GHUB integration scripts
├── src/                    # Build system
│   ├── Makefile           # Build commands
│   └── readme.txt         # Build instructions
├── LICENSE                 # MIT License
└── README.md              # This file

Building the Book Locally

# create a kernal and activate the icesee environment
./tools/go.icesee1 && source ./tools/create_icesee1_environment_yml.sh

# View the built book (opens on localhost:8080)
cd icesee_jupyter_book/_build/html
python -m http.server 8080

ICESEE Core Integration

This repository integrates with the main ICESEE codebase:

• Core source: ICESEE
• Integration method: Git subtree in external/ICESEE/
• Version pinning: Tracks the main branch for reproducibility (recommended: pin by tag for releases)

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Copyright (c) 2026 ICESEE, Brian Kyanjo, Alexander Robel

Citation

If you use ICESEE in your research, please cite:

@software{icesee2026,
  author = {Kyanjo, Brian and Robel, Alexander},
  title = {ICESEE: Ice-sheet Coupled Ensemble Simulator and Estimator},
  year = {2026},
  url = {https://github.com/ICESEE-project/ICESEE}
}

Support

• Issues: GitHub Issues
• Documentation: ICESEE Wiki
• GHUB Support: https://theghub.org/support

Acknowledgments

This work builds upon the ICESEE framework and leverages GHUB infrastructure for cloud-based scientific computing.

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Maintained by: Brian Kyanjo
Project: ICESEE (Ice-sheet Coupled Ensemble Simulator and Estimator)