The conditional nonlinear optimal perturbation (CNOP) technique is a useful tool for studying the limits of predictability in numerical weather forecasting and climate predictions. The CNOP is the optimal combined mode of the initial and model parameter perturbations that induce the largest departure from a given reference state. The CNOP has two special cases: the CNOP-I is linked to initial perturbations and has the largest nonlinear evolution at the time of prediction, while the other case, CNOP-P, is related to the parameter perturbations that cause the largest departure from a given reference state at a given future time. Solving the CNOPs of a numerical model is a mathematical problem. In this paper, we calculate the CNOP, CNOP-I, and...
We examine differential equations where nonlinearity is a result of the advection part of the total ...
International audienceWithin a theoretical model context, the sensitivity and instability of the gra...
The Lorenz-63 model has been frequently used to inform our understanding of the Earth's climate and ...
International audienceConditional nonlinear optimal perturbation (CNOP) is proposed to study the pre...
Abstract. Conditional nonlinear optimal perturbation (CNOP) is proposed to study the predictability ...
In the stability, sensitivity and predictability studies in geophysical fluid dynamics, linear singu...
Abstract The analysis of the growth of initial perturbations in dynamical systems is an important as...
In this paper, we propose a sampling algorithm based on state-of-the-art statistical machine learnin...
Conditional nonlinear optimal perturbation (CNOP) has been widely applied to study the predictabilit...
Neste trabalho estudamos as aplicações do método do Gradiente Espectral Projetado (SPG) em Meteorolo...
In the numerical prediction of weather or climate events, the uncertainty of the initial values and/...
[1] Seasonal dependence of initial error growth for El Niño-Southern Oscillation (ENSO) in Zebiak-C...
Model error, which results from model parameters, can cause the nonnegligible uncertainty in the Nor...
In this paper, we study the development of finite amplitude perturbations on linearly stable steady ...
A number of problems, arising from both theoretical research in atmospheric and oceanic sciences and...
We examine differential equations where nonlinearity is a result of the advection part of the total ...
International audienceWithin a theoretical model context, the sensitivity and instability of the gra...
The Lorenz-63 model has been frequently used to inform our understanding of the Earth's climate and ...
International audienceConditional nonlinear optimal perturbation (CNOP) is proposed to study the pre...
Abstract. Conditional nonlinear optimal perturbation (CNOP) is proposed to study the predictability ...
In the stability, sensitivity and predictability studies in geophysical fluid dynamics, linear singu...
Abstract The analysis of the growth of initial perturbations in dynamical systems is an important as...
In this paper, we propose a sampling algorithm based on state-of-the-art statistical machine learnin...
Conditional nonlinear optimal perturbation (CNOP) has been widely applied to study the predictabilit...
Neste trabalho estudamos as aplicações do método do Gradiente Espectral Projetado (SPG) em Meteorolo...
In the numerical prediction of weather or climate events, the uncertainty of the initial values and/...
[1] Seasonal dependence of initial error growth for El Niño-Southern Oscillation (ENSO) in Zebiak-C...
Model error, which results from model parameters, can cause the nonnegligible uncertainty in the Nor...
In this paper, we study the development of finite amplitude perturbations on linearly stable steady ...
A number of problems, arising from both theoretical research in atmospheric and oceanic sciences and...
We examine differential equations where nonlinearity is a result of the advection part of the total ...
International audienceWithin a theoretical model context, the sensitivity and instability of the gra...
The Lorenz-63 model has been frequently used to inform our understanding of the Earth's climate and ...