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.
Core assimilation cycle#
Most ICESEE applications follow the same sequence:
Initialize an ensemble of model states and/or parameters
Forecast step: propagate each ensemble member forward with the model
Observation operator: map model variables into observation space
Analysis step: update the ensemble using EnKF-style filtering
Repeat over time as new observations become available
This modular structure allows the same assimilation engine to be reused across different models.
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.
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.
Where to learn more#
Implementation details and developer documentation are maintained in the ICESEE Wiki: