3 citations · 5 across the 2 of their papers we have counts for
6 papers
On time-parallel preconditioning for the state formulation of incremental weak constraint 4D-Var
Ieva Daužickaitė, Amos S. Lawless, Jennifer A. Scott +1
Using a high degree of parallelism is essential to perform data assimilation efficiently. The state formulation of the incremental weak constraint four-dimensional variational data…
Model uncertainty estimation using the expectation maximization algorithm and a particle flow filter
María Magdalena Lucini, Peter Jan van Leeuwen, Manuel Pulido
Model error covariances play a central role in the performance of data assimilation methods applied to nonlinear state-space models. However, these covariances are largely unknown…
Spectral estimates for saddle point matrices arising in weak constraint four-dimensional variational data assimilation
Ieva Daužickaitė, Amos S. Lawless, Jennifer A. Scott +1
We consider the large-sparse symmetric linear systems of equations that arise in the solution of weak constraint four-dimensional variational data assimilation, a method of high in…
Kernel embedded nonlinear observational mappings in the variational mapping particle filter
Manuel Pulido, Peter Jan vanLeeuwen, Derek J. Posselt
Recently, some works have suggested methods to combine variational probabilistic inference with Monte Carlo sampling. One promising approach is via local optimal transport. In this…
Rainfall nowcasting by combining radars, microwave links and rain gauges
Blandine Bianchi, Peter Jan van Leeuwen, Robin J. Hogan +1
The objective of this work is to provide high-resolution rain rate maps at short lead-time forecasts (nowcasts) necessary to anticipate flooding and properly manage sewage systems…
Multiplicative non-Gaussian model error estimation in data assimilation
Sahani Pathiraja, Peter Jan van Leeuwen
Model uncertainty quantification is an essential component of effective data assimilation. Model errors associated with sub-grid scale processes are often represented through stoch…