Assimilation research of coastal acoustic tomography data in the Bali Strait, Indonesia

Coastal Acoustic Tomography (CAT) is an effective tool to observe the flow field in the large offshore range using high-frequency acoustic signals, of which direct observation range is still limited. The numerical ocean model provides a large-scale ocean background field with simulation errors, and the resolution and accuracy of the flow field results can be improved by assimilating the CAT data with the ocean background results. In this paper, we applied a method to obtain a larger range of two-dimensional ocean flow field results by fitting ocean-mode flow field results using Stream Function and assimilating CAT data using the Ensemble Kalman Filtering algorithm. The assimilation study used the unstructured grid Finite-Volume Community Ocean Model (FVCOM) as the background field, and the four CAT stations experiment conducted in Bali Strait, Indonesia, from 1st to 3rd June 2016 as the observational data. After fitting background field by Stream Function and assimilating CAT data, the two-dimensional flow field in Bali Strait is obtained. The assimilation results were compared with those of the same period of observation and tide level data, which is found that the flow function fitted and assimilated flow field can more accurately describe the high and low tides and flow conditions in the Bali Strait. By introducing the functional relationship between the CAT data and the flow field it can effectively reduce the error of the ocean model and the sparsity of the original observation data.