4 papers
A modified particle filter that reduces weight collapse
Shay Gilpin, Michael Herty
Particle filters are a widely used Monte Carlo based data assimilation technique that estimates the probability distribution of a system's state conditioned on observations through…
Numerical study of high-dimensional covariance estimation and localization for data assimilation
Shay Gilpin, Matthias Morzfeld, Kevin K. Lin
Covariance localization is a critical component of ensemble-based data assimilation (DA) and many current localization schemes simply dampen correlations as a function of distance.…
Inaccuracy of Ensemble-Based Covariance Propagation, Beyond Sampling Error
Shay Gilpin
Modern data assimilation schemes typically use the same discrete dynamical model to evolve the state estimate in time also to approximate the evolution, or propagation, of the esti…
Continuum covariance propagation for understanding variance loss in advective systems
Shay Gilpin, Tomoko Matsuo, Stephen E. Cohn
Motivated by the spurious variance loss encountered during covariance propagation in atmospheric and other large-scale data assimilation systems, we consider the problem for state…