Lyapunov exponents are well-known characteristic numbers that describe growth rates of
perturbations applied to a trajectory of a dynamical system in different state space directions …

A local particle filter (LPF) is introduced that outperforms traditional ensemble Kalman filters
in highly nonlinear/non-Gaussian scenarios, both in accuracy and computational cost. The …

We study a simplified coupled atmosphere-ocean model using the formalism of covariant
Lyapunov vectors (CLVs), which link physically-based directions of perturbations to …

We identify the dynamical drivers of systematic changes in persistent quasi-stationary states
(regimes) of the Southern Hemisphere troposphere and their secular trends. We apply a …

The stability properties of intermediate-order climate models are investigated by computing
their Lyapunov exponents (LEs). The two models considered are PUMA (Portable University …

We investigate the ability to discover data assimilation (DA) schemes meant for chaotic
dynamics with deep learning. The focus is on learning the analysis step of sequential DA …

Data assimilation for systems possessing many scales of motions is a substantial
methodological and technological challenge. Systems with these features are found in many …

Results are presented from an ensemble prediction study (EPS) of the East Australian
Current (EAC) with a specific focus on the examination of the role of dynamical instabilities …

A leading goal for climate science and weather risk management is to accurately model both
the physics and statistics of extreme events. These two goals are fundamentally at odds: the …

The ensemble Kalman filter (EnKF) is susceptible to losing track of observations, or
'diverging', when applied to large chaotic systems such as atmospheric and ocean models …