A new ensemble ocean data assimilation system, developed for the Predictive Ocean
Atmosphere Model for Australia (POAMA), is described. The new system is called PEODAS …

A simple approach to the estimation of representation error (RE) of sea level (η),
temperature (T), and salinity (S) observations for ocean data assimilation is described. It is …

This article shows how one can formulate the representation problem starting from Bayes'
theorem. The purpose of this article is to raise awareness of the formal solutions, so that …

This paper studies the sensitivity of global ocean analyses to two flow‐dependent
formulations of the background‐error standard deviations (σb) for temperature and salinity in …

An Ensemble Adjustment Kalman Filter for the CCSM4 Ocean Component in: Journal of Climate
Volume 26 Issue 19 (2013) Jump to Content Jump to Main Navigation Logo Logo Logo Logo …

Major forecast errors on the background error covariance from initial conditions, atmospheric
forcing, model open boundary conditions, and the river discharges are examined in a …

Monitoring sea surface salinity (SSS) is important for understanding and forecasting the
ocean circulation. It is even crucial in the context of the intensification of the water cycle. Until …

The ensemble Kalman filter (EnKF) depends on a set of ensemble forecasts to calculate the
background error covariances. Without model error perturbations and the inflation of forecast …

The ocean state off Oregon-Washington, US West coast, is highly variable in time. Under
these conditions the assumption made in traditional 4-dimensional variational data …

An Ensemble Approach for the Estimation of Observational Error Illustrated for a Nominal 1
Global Ocean Model in: Monthly Weather Review Volume 144 Issue 5 (2016) Jump to …