This article reviews the past 15 years of developments in regional ocean data assimilation. A variety of scientific, management, and safety-related objectives motivate marine scientists to characterize many ocean environments, including coastal regions. As in weather prediction, the accurate representation of physical, chemical, and/or biological properties in the ocean is challenging. Models and observations alone provide imperfect representations of the ocean state, but together they can offer improved estimates. Variational and sequential methods are among the most widely used in regional ocean systems, and there have been exciting recent advances in ensemble and four-dimensional variational approaches. These techniques are increasingly being tested and adapted for biogeochemical applications.

Species distribution models are now widely used in conservation and management to predict suitable habitat for protected marine species. The primary sources of dynamic habitat data have been in situ and remotely sensed oceanic variables (both are considered “measured data”), but now ocean models can provide historical estimates and forecast predictions of relevant habitat variables such as temperature, salinity, and mixed layer depth. To assess the performance of modeled ocean data in species distribution models, we present a case study for cetaceans that compares models based on output from a data assimilative implementation of the Regional Ocean Modeling System (ROMS) to those based on measured data. Specifically, we used seven years of cetacean line-transect survey data collected between 1991 and 2009 to develop predictive habitat-based models of cetacean density for 11 species in the California Current Ecosystem. Two different generalized additive models were compared: one built with a full suite of ROMS output and another built with a full suite of measured data. Model performance was assessed using the percentage of explained deviance, root mean squared error (RMSE), observed to predicted density ratios, and visual inspection of predicted and observed distributions. Predicted distribution patterns were similar for models using ROMS output and measured data, and showed good concordance between observed sightings and model predictions. Quantitative measures of predictive ability were also similar between model types, and RMSE values were almost identical. The overall demonstrated success of the ROMS-based models opens new opportunities for dynamic species management and biodiversity monitoring because ROMS output is available in near real time and can be forecast.

The generation and underlying dynamics of late‐spring temperature and salinity fronts at the mouth of Block Island Sound in the Mid‐Atlantic Bight are examined using a three‐dimensional general circulation model. The model is forced by tidal velocities at its outer boundaries, an estuary/shelf salinity gradient, and surface warming. Runs representing the period from 18 April to 23 May 2000 are investigated through direct examination of the model results and diagnostic calculations that isolate individual processes. Three mechanisms are identified as significantly contributing to the location and structure fronts observed in this region. Vertical mixing produces the necessary horizontal temperature structure within and near Block Island Sound, but the southern frontal boundary is further affected by the subtidal circulation, which is divided into two components defined using a momentum budget analysis. Tidal currents over the complex bathymetry leads to nonlinearly rectified flows, including a headland eddy south of Montauk Point. Circumscribing this feature is a baroclinically adjusted, buoyant outflow from the Long Island Sound estuary. While modifications to the buoyancy forcing can modify the frontal position, it necessarily remains offshore of the tidally driven circulation. Analysis of the salinity anomaly fluxes indicates that both tidal and mean advection are responsible for the overall transport of buoyancy.

The use of a one‐layer inverse model to improve the fidelity of a three‐dimensional primitive equation model is explored in the region just south of Block Island Sound in the Middle Atlantic Bight. The inverse model, based on the theory of Bennett and McIntosh, produces a solution to the linear, shallow‐water equations that minimizes model‐data misfit and the resulting open boundary velocity variance. The Massachusetts Institute of Technology general circulation model is configured regionally and includes realistic tidal forcing, surface warming, and wind stress, as well as climatological buoyancy gradients between the estuary and the shelf. A 15 day record from two acoustic Doppler current profiler moorings, deployed in spring 2000 south of the entrance of Block Island Sound, allows assessment of the forward model and provides data to improve subtidal boundary conditions. Model‐data misfit decreases notably for both the depth‐averaged and vertically sheared flow, indicating that improvements to the depth‐averaged circulation positively impact the three‐dimensional circulation in this shallow shelf environment. The role of wind stress in modulating the position of the buoyant plume that characterizes the regional hydrography is also examined. A distinctly three‐dimensional circulation is observed, with a growing offshore plume at the mouth of the sound during northward stress followed by rapid alongshelf advection during periods of southwestward or weak winds. Although, on the timescales considered here, wind stress forcing dominates the buoyancy‐driven motion, it is overwhelmed by the tidally driven rectified flow just south of Montauk Point, which provides the inshore boundary of the front even in downwelling favorable conditions.

Ocean observing systems in coastal, shelf and marginal seas collect diverse oceanographic information supporting a wide range of socioeconomic needs, but observations are necessarily sparse in space and/or time due to practical limitations. Ocean analysis and forecast systems capitalize on such observations, producing data-constrained, four-dimensional oceanographic fields. Here we review efforts to quantify the impact of ocean observations, observing platforms, and networks of platforms on model products of the physical ocean state in coastal regions. Quantitative assessment must consider a variety of issues including observation operators that sample models, error of representativeness, and correlated uncertainty in observations. Observing System Experiments, Observing System Simulation Experiments, representer functions and array modes, observation impacts, and algorithms based on artificial intelligence all offer methods to evaluate data-based model performance improvements according to metrics that characterize oceanographic features of local interest. Applications from globally distributed coastal ocean modeling systems document broad adoption of quantitative methods, generally meaningful reductions in model-data discrepancies from observation assimilation, and support for assimilation of complementary data sets, including subsurface in situ observation platforms, across diverse coastal environments.