Interactive tutorials on combining dynamical models with observations to estimate the true state of a chaotic system. Each notebook is self-contained and runs entirely in your browser via WebAssembly — no installation required.
A twin-experiment walkthrough of three foundational data assimilation algorithms on the chaotic Lorenz 63 system. Starting from a perturbed initial condition, synthetic observations are assimilated to recover the true trajectory — illustrating how each algorithm exploits the model and observations differently.
Replacing or augmenting the dynamical forecast model with a neural network emulator trained on ensemble trajectories. Explores how differentiable emulators enable gradient-based 4DVAR without a hand-coded adjoint, and how model error affects the analysis cycle.
Score-based generative models (diffusion / flow-matching) can encode complex, non-Gaussian background error distributions learned from model climatology. This tutorial replaces the Gaussian 𝐁 matrix with a learned score function and samples the posterior via Langevin dynamics.