143 citations · 209 across the 7 of their papers we have counts for
7 papers
Alignments of Geophysical Fields: a differential geometry perspective
Yicun Zhen, Valentin Resseguier, Bertrand Chapron
To estimate the displacements of physical state variables, the physics principles that govern the state variables must be considered. Technically, for a certain class of state vari…
Physically Constrained Covariance Inflation from Location Uncertainty
Yicun Zhen, Valentin Resseguier, Bertrand Chapron
Motivated by the concept of ``location uncertainty", initially introduced in \cite{Memin2013FluidFD}, a scheme is sought to perturb the ``location" of a state variable at every for…
Bridging Koopman Operator and time-series auto-correlation based Hilbert-Schmidt operator
Yicun Zhen, Bertrand Chapron, Etienne Mémin
Given a stationary continuous-time process , the Hilbert-Schmidt operator can be defined for every finite \cite{Vautard1989SingularSA}. Let be the eigenval…
Eigenvalues of Autocovariance Matrix: A Practical Method to Identify the Koopman Eigenfrequencies
Yicun Zhen, Bertrand Chapron, Etienne Memin +1
To infer eigenvalues of the infinite-dimensional Koopman operator, we study the leading eigenvalues of the autocovariance matrix associated with a given observable of a dynamical s…
Using local dynamics to explain analog forecasting of chaotic systems
P Platzer, P. Yiou, P. Naveau +4
Analogs are nearest neighbors of the state of a system. By using analogs and their successors in time, one is able to produce empirical forecasts. Several analog forecasting method…
A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation
Pierre Tandeo, Pierre Ailliot, Marc Bocquet +4
Data assimilation combines forecasts from a numerical model with observations. Most of the current data assimilation algorithms consider the model and observation error terms as ad…
Adaptive Error Covariances Estimation Methods for Ensemble Kalman Filters
Yicun Zhen, John Harlim
This paper presents a computationally fast algorithm for estimating, both, the system and observation noise covariances of nonlinear dynamics, that can be used in an ensemble Kalma…