Multivariate Data Assimilation at a Partially Mixed Estuary

Abstract

Abstract In 2013 and 2014, multiple field excursions of varying scope were concentrated on the Columbia River, a highly energetic, partially mixed estuary. These experiments included surface drifter and synthetic aperture radar (SAR) measurements during the ONR RIVET-II experiment, and a novel animal tracking effort that samples oceanographic data by employing cormorants tagged with biologging devices. In the present work, several different data types from these experiments were combined in order to test an iterative, ensemble-based inversion methodology at the mouth of the Columbia River (MCR). Results show that, despite inherent limitations of observation and model accuracy, it is possible to detect dynamically relevant bathymetric features such as large shoals and channels while originating from a linear, featureless prior bathymetry in a partially mixed estuary by inverting surface current and gravity wave observations with a 3D hydrostatic ocean model. Bathymetry estimation skill depends on two factors: location (i.e., differing estimation quality inside versus outside the MCR) and observation type (e.g., surface currents versus significant wave height). Despite not being inverted directly, temperature and salinity outputs in the hydrodynamic model improved agreement with observations after bathymetry inversion.