Asymptotic behavior of the forecast-assimilation process with unstable dynamics

02/06/2022

∙

by Dan Crisan, et al.

∙

Imperial College London

∙

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.

READ FULL TEXT

Asymptotic behavior of the forecast-assimilation process with unstable dynamics

Hidden Degrees of Freedom in Implicit Vortex Filaments

A Particle Filter for Stochastic Advection by Lie Transport (SALT): A case study for the damped and forced incompressible 2D Euler equation

Data assimilation for a quasi-geostrophic model with circulation-preserving stochastic transport noise

A Bayesian Hierarchical Model Framework to Quantify Uncertainty of Tropical Cyclone Precipitation Forecasts

When Does More Regularization Imply Fewer Degrees of Freedom? Sufficient Conditions and Counter Examples from Lasso and Ridge Regression

Well-Balanced and Asymptotic Preserving IMEX-Peer Methods

Theoretical analysis and numerical approximation for the stochastic thermal quasi-geostrophic model

Asymptotic behavior of the forecast-assimilation process with unstable dynamics

Related Research

Hidden Degrees of Freedom in Implicit Vortex Filaments

A Particle Filter for Stochastic Advection by Lie Transport (SALT): A case study for the damped and forced incompressible 2D Euler equation

Data assimilation for a quasi-geostrophic model with circulation-preserving stochastic transport noise

A Bayesian Hierarchical Model Framework to Quantify Uncertainty of Tropical Cyclone Precipitation Forecasts

When Does More Regularization Imply Fewer Degrees of Freedom? Sufficient Conditions and Counter Examples from Lasso and Ridge Regression

Well-Balanced and Asymptotic Preserving IMEX-Peer Methods

Theoretical analysis and numerical approximation for the stochastic thermal quasi-geostrophic model