Joint ECMWF/OceanPredict workshop on Advances in Ocean Data Assimilation

Machine Learning has proved to be an innovative, disruptive set of technologies capable of revolutionising many fields of applied science and engineering. A crucial scientific question is whether Machine Learning can have the same impact on Earth system assimilation and prediction, both in a holistic sense and for improving the separate Earth system components. The recent ECMWF-ESA Machine...

We investigate the impact of large climatological biases in the tropical Atlantic on reanalysis and seasonal prediction performance using the Norwegian Climate Prediction Model (NorCPM) in a standard and an anomaly coupled configuration. Anomaly coupling corrects the climatological surface wind and sea surface temperature (SST) fields exchanged between oceanic and atmospheric models, and...

Generating optimal perturbations is a key requirement of several data assimilation schemes. Here, we present a newly developed stochastic physics package for ocean models, implemented in the NEMO ocean general circulation model. The package includes three schemes applied simultaneously: stochastically perturbed parameterization tendencies (SPPT), stochastically perturbed parameters (SPP) and...

An interactive multi-model ensemble (named as supermodel) based on three state-of-the-art earth system models (i.e., NorESM, MPIESM and CESM) is developed. The models are synchronized every month by data assimilation. The data assimilation method used is the Ensemble Optimal Interpolation (EnOI) scheme, for which the covariance matrix is constructed from a historical ensemble. The assimilated...

In this presentation we focus on atmosphere-coupled interactions
characterizing the high temporal/spatial variability in temperature, salinity. For example, the near-surface temperature goes through diurnal cycles in sea surface temperature (SST) due to the exchange of heat and momentum. In addition, near-surface salinity changes rapidly with rain and wind-mixing events. For a proper...

Accurate parameter estimation of a global tide model benefits from the use of long time-series for many locations. However, with the number of measurements increasing also the computational times and memory requirements for the assimilation increase, especially for the ensemble-based methods that assimilate the measurements at one batch. We developed a memory-efficient and...