Asymptotic behavior of the forecast-assimilation process with unstable dynamics
arXiv:2202.02862 · doi:10.1063/5.0105590
Abstract
Extensive numerical evidence shows that the assimilation of observations has a stabilizing effect on unstable dynamics, in numerical weather prediction and elsewhere. In this paper, we apply mathematically rigorous methods to showing why this is so. Our stabilization results do not assume a full set of observations and we provide examples where it suffices to observe the model's unstable degrees of freedom.
Revised version
References in corpus (8)
- Climate dynamics and fluid mechanics: Natural variability and related uncertainties
- Continuous Data Assimilation for the 2D Bénard Convection through Velocity Measurements Alone
- Ranking IPCC Models Using the Wasserstein Distance
- Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations
- Uniform observability of hidden Markov models and filter stability for unstable signals
- Data assimilation as a nonlinear dynamical systems problem: Stability and convergence of the prediction-assimilation system
- Data assimilation for a quasi-geostrophic model with circulation-preserving stochastic transport noise
- Stability of nonlinear filters in nonmixing case