Four-Dimensional Variational Assimilation of Satellite Temperature and Sea Level Data in the Coastal Ocean and Adjacent Deep Sea

Abstract

Incremental, Strong constraint, 4-dimensional variational data assimilation is used to initialize operational forecast models of mesoscale ocean circulation in continental shelf and associated boundary current regimes. In the East Australia Current and the Mid-Atlantic Bight, data assimilation in the deep ocean adjacent to the shelf imposes the influence of remote-ocean forcing on coastal dynamics. Observations assimilated are satellite surface temperature (SST), satellite altimeter sea level anomalies (SLA), and subsurface temperature and salinity from ships, autonomous underwater vehicles and/or profiling floats. The models use boundary data from operational basin-scale circulation models and weather forecast meteorological forcing. Control variables of the data assimilation are the initial conditions of each assimilation window, and the model trajectory through each interval is deemed the best-estimate analysis for initializing the subsequent forecast. We evaluate model skill from a large set of multi-day forecasts starting from different initial mesoscale states. Forecast skill is enhanced, and uncertainty reduced, when empirical statistical subsurface pseudo-observations and/or so-called balance constraints are used to augment surface satellite data.