P-4

Experiment design to investigate the SLA and SST data assimilation impact over the South Atlantic Ocean

L. N. Lima (National Institute For Space Research), L. P. Pezzi (National Institute For Space Research), C. A. S. Tanajura (Oceanographic Modeling and Observation Network)

Data assimilation methods are important scientific tools for meteorology and oceanography. Specifically, the sea level anomaly (SLA) and sea surface temperature (SST) assimilation have shown great improvements on finding a best state of the oceanic model. This study aims to present results about SLA assimilation into Hybrid Coordinate Ocean Model where the Ensemble Optimal Interpolation (EnOI) method is used over Atlantic Ocean. Different observation windows from 1 to 7 days were employed considering the super-observation approach to treat the relatively high number and resolution of observations. These experiments presented RMSD with respect to observations of 0.07 m, while the experiment without data assimilation reached values of 0.105 m, approximately. The SLA assimilation could improve the representation of mesoscale circulation over the domain. A sequence of this first effort is to conduct SLA and SST assimilation using a more sophisticated method. This will be introduced by using the Local Ensemble Transform Kalman Filter (LETKF) with the Regional Ocean Modeling System. It is believed that using LETKF and a high resolution regional model will lead to a suitable alternative to improve previous results obtained by EnOI and to contribute for a better understanding of the oceanic system over the South Atlantic Ocean.