Adjusting Internal Model Errors through Ocean State Estimation
نویسندگان
چکیده
منابع مشابه
Online State Space Model Parameter Estimation in Synchronous Machines
The purpose of this paper is to present a new approach based on the Least Squares Error method for estimating the unknown parameters of the nonlinear 3rd order synchronous generator model. The proposed method uses the mathematical relationships between the machine parameters and on-line input/output measurements to estimate the parameters of the nonlinear state space model. The field voltage is...
متن کاملState Estimation of the Labrador Sea with a Coupled Sea Ice-Ocean Adjoint Model
Sea ice (SI) and ocean variability in marginal polar and subpolar seas are closely coupled. SI variability in the Labrador Sea is of climatic interest because of its relationship to deep convection/mode water formation, carbon sequestration, and Northern Hemisphere atmospheric patterns. Historically, quantifying the link between the region’s observed SI and oceanic variability has been limited ...
متن کاملNon-Gaussian Test Models for Prediction and State Estimation with Model Errors
Turbulent dynamical systems involve dynamics with both a large dimensional phase space and a large number of positive Lyapunov exponents. Such systems are ubiquitous in applications in contemporary science and engineering where statistical ensemble prediction and real-time filtering/state estimation are needed despite the underlying complexity of the system. Statistically exactly solvable test ...
متن کاملControllability, not chaos, key criterion for ocean state estimation
The Lagrange multiplier method for combining observations and models (i.e., the adjoint method or “4DVAR”) has been avoided or approximated when the numerical model is highly nonlinear or chaotic. This approach has been adopted primarily due to difficulties in the initialization of low-dimensional chaotic models, where the search for optimal initial conditions by gradient-descent algorithms is ...
متن کاملTest models for improving filtering with model errors through stochastic parameter estimation
The filtering skill for turbulent signals from nature is often limited by model errors created by utilizing an imperfect model for filtering. Updating the parameters in the imperfect model through stochastic parameter estimation is one way to increase filtering skill and model performance. Here a suite of stringent testmodels for filteringwith stochastic parameter estimation is developed based ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Physical Oceanography
سال: 2005
ISSN: 1520-0485,0022-3670
DOI: 10.1175/jpo2733.1