# ICESEE Workflow Overview

ICESEE is organized around a consistent ensemble data assimilation loop.
This page summarizes the workflow at a conceptual level, independent of the specific model.

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## Core assimilation cycle

Most ICESEE applications follow the same sequence:

1. **Initialize an ensemble** of model states and/or parameters  
2. **Forecast step:** propagate each ensemble member forward with the model  
3. **Observation operator:** map model variables into observation space  
4. **Analysis step:** update the ensemble using EnKF-style filtering  
5. **Repeat** over time as new observations become available  

This modular structure allows the same assimilation engine to be reused across different models.

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## Model component vs. assimilation engine

A key design principle is the separation between:

- the **assimilation engine** (ensemble update logic), and  
- the **model backend** (Lorenz-96, flowline, ISSM, Icepack, etc.)

Only the model forecast and observation mapping change between applications.

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## Observations and inference targets

ICESEE supports workflows where observations constrain:

- time-evolving ice-sheet state variables  
- uncertain parameters (e.g., basal friction)  
- synthetic or real geophysical datasets  

The GHUB tutorials focus on portable examples, while larger-scale applications are documented upstream.

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## Where to learn more

Implementation details and developer documentation are maintained in the ICESEE Wiki:

- https://github.com/ICESEE-project/ICESEE/wiki