Now showing items 1-6 of 6
Using the Extended Kalman Filter with a Multilayer Quasi-Geostrophic Ocean Model
(American Geophysical Union, 1992-11-15)
The formulation of the extended Kalman filter for a multilayer nonlinear quasi-geostrophic ocean circulation model is discussed. The nonlinearity in the ocean model leads to an approximative equation for error covariance ...
Open Boundary Conditions for the Extended Kalman Filter With a Quasi-Geostrophic Ocean Model
(American Geophysical Union, 1993-05-18)
The formulation of consistent boundary conditions for the quasi-geostrophic (QG) model with an extended Kaiman filter in a data assimilation scheme is discussed. To form a well-posed boundary value problem for the QG model, ...
Inverse Methods and Data Assimilation in Nonlinear Ocean Models
An overview is given of the current status of inverse methods and data assimilation for nonlinear ocean models. The inverse theory for time dependent dynamical models is formulated and the most promising solution methods ...
The impact of ensemble filter definition on the assimilation of temperature profiles in the tropical Pacific
(Royal Meteorological Society, 2005-10)
The traditional analysis scheme in the Ensemble Kalman Filter (EnKF) uses a stochastic perturbation or randomization of the measurements which ensures a correct variance in the updated ensemble. An alternative so called ...
Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics
(American Geophysical Union, 1994-05-15)
A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding ...
Parameter Estimation Solving a Weak Constraint Variational Formulation for an Ekman Model
(American Geophysical Union, 1997-06-15)
A weak constraint variational formulation is used for inverse calculations and parameter estimation in a one-dimensional Ekman model. When parameters in the model are allowed to contain errors, the inverse problem becomes ...