Joint ECMWF/OceanPredict workshop on Advances in Ocean Data Assimilation

We present mathematical arguments and experimental evidence that suggest that Gaussian approximations of posterior distributions are appropriate even if the physical system under consideration is nonlinear. The reason for this is a regularizing effect of the observations that can turn multi-modal prior distributions into nearly Gaussian posterior distributions. This has important ramifications...

This presentation will give an overview of recent developments in NEMOVAR that took place within the Copernicus-funded project ERGO. Its aim is to improve ocean data assimilation capabilities at ECMWF, used in both initialization of seasonal forecasts and generation of coupled Earth System reanalyses. In particular it has significantly improved NEMOVAR’s ensemble generation capabilities, which...

This presentation provides an overview of methods for using ensembles to define background-error covariances in variational data assimilation (DA) with an emphasis on the global ocean. The methods that are described have been developed for NEMOVAR in support of operational DA at ECMWF and the Met Office. Various localized-ensemble and hybrid formulations of the background-error covariance...

This presentation will summarize the work by the NEMOVAR consortium (ECMWF, CERFACS, UK Met Office, INRIA) to develop an ensemble-variational data assimilation system for the NEMO model enabling effective assimilation of ocean observations. A holistic approach has been adopted by revisiting our static B matrix formulation, developing various flavours of a flow dependent B matrix and...

A global ocean and sea-ice ensemble forecasting system is being developed based on the present operational FOAM (Forecasting Ocean Assimilation Model) system run at the Met Office. This uses a 1/4° resolution NEMO ocean model and CICE sea-ice model, and assimilates data using the NEMOVAR system. NEMOVAR is primarily a variational data assimilation system, but now has the capability to perform...