Our People
Ivo Pasmans
Postdoctoral Research Assistant
Data Assimilation
Research interests
My field of interest is the development of novel data assimilation algorithms to exploit the discontinous Galerkin numerical core of the new neXtSIM_DG sea ice model and ways to deal with the non-Gaussianity of the errors in such a model.
Recent publications
Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble. 2024-09-01
DOI: https://doi.org/10.1063/5.0230837
Tailoring data assimilation to discontinuous Galerkin models. 2024-07
DOI: https://doi.org/10.1002/qj.4737
Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology. 2024-05-14
URI: https://centaur.reading.ac.uk/116449/
Ocean drifter velocity data assimilation, Part 1: Formulation and diagnostic results. 2023-06
DOI: https://doi.org/10.1016/j.ocemod.2023.102195 ISSN: https://portal.issn.org/resource/ISSN/1463-5003
Ensemble 4DVAR (En4DVar) data assimilation in a coastal ocean circulation model. Part II: Implementation offshore Oregon–Washington, USA. 2020-10
DOI: https://doi.org/10.1016/j.ocemod.2020.101681 ISSN: https://portal.issn.org/resource/ISSN/1463-5003
Ensemble of 4DVARs (En4DVar) data assimilation in a coastal ocean circulation model, Part I: Methodology and ensemble statistics. 2019-12
DOI: https://doi.org/10.1016/j.ocemod.2019.101493 ISSN: https://portal.issn.org/resource/ISSN/1463-5003
Why Gliders Appreciate Good Company: Glider Assimilation in the Oregon‐Washington Coastal Ocean 4DVAR System With and Without Surface Observations. 2019-01
DOI: https://doi.org/10.1029/2018JC014230
A Monte Carlo Background Covariance Localization Method for an Ensemble–Variational Assimilation System. 2017-11
