¡CEDA! is a Python library for the estimation of covariance matrices Q and R in data assimilation. The estimation procedure is based on the Expectation-Maximization (EM) algorithm for nonlinear state-space models resolved by two different Kalman procedures, the extended (EKS) and the ensemble (EnKS) Kalman smoothers. We test the algorithms on two dynamical models: the Lorenz-63 and Lorenz-96 chaotic systems.
A description and a test of the code is given in the ipython notebook "test_CEDA.ipynb".
Please contact Pierre Tandeo (pierre.tandeo@imt-atlantique.fr) in case of bugs.
This Python library is attached to the following publication (http://onlinelibrary.wiley.com/doi/10.1002/qj.3048/full): Dreano, D., Tandeo, P., Pulido, M., Ait‐El‐Fquih, B., Chonavel, T., & Hoteit, I. (2017). Estimating Model‐Error Covariances in Nonlinear State‐Space Models using Kalman Smoothing and the Expectation–Maximization Algorithm. Quarterly Journal of the Royal Meteorological Society, 143(705), 1877-1885. If you use this library, please do not forget to cite our work.