Near-Real-Time Earth Observation Data Assimilation for Ocean Currents

This paper presents a robust approach to assimilate Earth Observation (EO) data into multi-model ensembles, specifically focusing on integrating High-Frequency (HF) Radar-derived ocean surface currents covering Galician coast of Spain. The methodology employs Ensemble Kalman filter (EnKF) approach to promptly and effectively adjust the estimates of ensemble models in comparison to the provided observations. However, degradation of results is observed after 12 hrs forecast, due to variations in the covariance matrix. The performance of this approach has been assessed through comparative analysis between the averaged ensembles and four specific ensembles, employing the Root Mean Square Error (RMSE) metric. Our findings indicate that the assimilation of EO data into large ensembles of ocean currents models yields superior results compared to employing a single model. To extend the system’s operational duration we propose the implementation of an inflation/regularization factor.