Joint ECMWF/OceanPredict workshop on Advances in Ocean Data Assimilation

A multi-timescale high-efficiency approximate EnKF (MSHea-EnKF), which consists of stationary, slow-varying, and fast-varying filter using the time series of a single-model solution, has been implemented in the Geophysical Fluid Dynamics Laboratory’s global fully coupled climate model (CM2.1) to increase the representation of low-frequency background error statistics and enhance the...

We introduce biogeochemical – ocean – sea ice state estimates in the Southern Ocean. Atmospheric fields are adjusted to fit observations from profiling floats, shipboard data, underway measurements, and satellites. These atmospheric adjustments shed light on biases in downwelling radiative fluxes in existing atmospheric reanalysis models. We demonstrate the validity of adjoint method...

In variational data assimilation, background-error covariance structures have the ability to spread information from an observed part of the system to unobserved parts. Hence an accurate specification of these structures is crucially important for the success of assimilation systems and therefore of forecasts that their outputs initiate. For oceanic models, background-error covariances have...

The multivariate Deterministic Ensemble Kalman Filter (DEnKF) has been implemented to assimilate physical and biological observations into a biogeochemical model of the Gulf of Mexico. First, the biogeochemical model component was tuned using BGC-Argo observations. Then, observations of sea surface height, sea surface temperature, and surface chlorophyll were assimilated and profiles of both...