10 citations as recorded by crossref.

1. A methodology to obtain model-error covariances due to the discretization scheme from the parametric Kalman filter perspective O. Pannekoucke et al. 10.5194/npg-28-1-2021
2. Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble M. Bocquet et al. 10.1063/5.0230837
3. Covariance kernels investigation from diffusive wave equations for data assimilation in hydrology T. Malou & J. Monnier 10.1088/1361-6420/ac509d
4. PDE-NetGen 1.0: from symbolic partial differential equation (PDE) representations of physical processes to trainable neural network representations O. Pannekoucke & R. Fablet 10.5194/gmd-13-3373-2020
5. Data assimilation using adaptive, non-conservative, moving mesh models A. Aydoğdu et al. 10.5194/npg-26-175-2019
6. Exploring the limits of ensemble forecasting via solutions of the Liouville equation for realistic geophysical models A. Hermoso et al. 10.1016/j.atmosres.2020.105127
7. Continuum Covariance Propagation for Understanding Variance Loss in Advective Systems S. Gilpin et al. 10.1137/21M1442449
8. Toward a multivariate formulation of the parametric Kalman filter assimilation: application to a simplified chemical transport model A. Perrot et al. 10.5194/npg-30-139-2023
9. SymPKF (v1.0): a symbolic and computational toolbox for the design of parametric Kalman filter dynamics O. Pannekoucke & P. Arbogast 10.5194/gmd-14-5957-2021
10. Boundary Conditions for the Parametric Kalman Filter Forecast M. Sabathier et al. 10.1029/2022MS003462