@article {arango_4d-var_2023,
	title = {4D-Var data assimilation in a nested model of the Mid-Atlantic Bight},
	journal = {Ocean Modelling},
	volume = {184},
	year = {2023},
	pages = {102201},
	abstract = {The Regional Ocean Modeling System (ROMS) 4-dimensional variational (4D-Var) data assimilation platform has been extended to include nested grid configurations using both one-way and two-way nesting strategies. The efficacy of this new ROMS utility is demonstrated in a model comprising three nested grids with horizontal refinement and configured for the Mid-Atlantic Bight. The three nested grids have a horizontal resolution ranging from \~{}7 km to \~{}0.8 km thereby capturing circulation regimes that span the Gulf Stream western boundary current, through the mesoscale eddy field, and down to the rapidly evolving and energetic submesoscale. These circulation regimes represent a challenge for any data assimilation system, and the nested 4D-Var system was found to perform well across the range of resolved space and time scales. The observational data used to constrain the ocean state estimates come from a wide range of remote sensing, in situ, and mobile platforms, along with the U.S. National Science Foundation{\textquoteright}s Ocean Observatories Initiative Pioneer Array. Several aspects of the system performance are explored and described here, including the fit of the model to the observations, the influence of data assimilation on the wave number spectra at the submesoscale, and the downscaling and upscaling of information captured by the observations.},
	keywords = {4D-Var, Data assimilation, Mid-Atlantic Bight, Nested grids, Pioneer Array},
	issn = {1463-5003},
	doi = {10.1016/j.ocemod.2023.102201},
	url = {https://www.sciencedirect.com/science/article/pii/S1463500323000422},
	author = {Arango, Hernan G. and Levin, Julia and Wilkin, John and Moore, Andrew M.}
}
@article {wilkin_data-assimilative_2022,
	title = {A data-assimilative model reanalysis of the U.S. Mid Atlantic Bight and Gulf of Maine: Configuration and comparison to observations and global ocean models},
	journal = {Progress in Oceanography},
	volume = {209},
	year = {2022},
	pages = {102919},
	abstract = {A 15-year reanalysis (2007{\textendash}2021) of circulation in the coastal ocean and adjacent deep sea of the northeast U.S. continental shelf is described. The analysis uses the Regional Ocean Modeling System (ROMS) and four-dimensional variational (4D-Var) data assimilation (DA) of observations from in situ platforms, coastal radars, and satellites. The reanalysis downscales open boundary information from the Copernicus Marine Environmental Monitoring Service (CMEMS) global analysis. The dynamic model is forced by regional meteorological analyses, observed daily river discharges, and harmonic tides that augment the open boundary conditions. A complementary analysis of the mean seasonal cycle of regional circulation, also computed using ROMS 4D-Var but with climatological mean observations and forcing, is used to reduce biases in the CMEMS boundary data and to provide a dynamically and kinematically constrained Mean Dynamic Topography to use in conjunction with the assimilation of satellite altimeter sea level anomaly observations. The configuration of ROMS 4D-Var used is described, presenting details of the comprehensive suite of observations assembled, data pre-processing and quality control procedures, and background and observation error hypotheses. Control variables of the DA are the initial conditions, surface forcing, and boundary conditions of a sequence of non-overlapping 3-day analysis cycles. Comparisons to a non-assimilative version of the same ROMS model configuration show the added skill brought by assimilation of local observations. The improvement that downscaling with assimilation achieves over ocean state estimates from CMEMS and the U.S. Naval Research Laboratory Global Ocean Forecast System (GOFS) is demonstrated by the reduction in residuals of the DA, and by comparison to independent (unassimilated) observations. Wherever data volumes allow, skill assessments are made with the respect to anomalies from the mean seasonal cycle to emphasize performance at the ocean mesoscale. To highlight the utility of the analysis to inform studies related to coastal sea level variability and marine ecosystems, comparisons are made to unassimilated coastal sea level gauges and novel observations from sensors on fishing gear. The assimilation of coastal satellite altimetry data produces coastal sea level results that are coherent with observations across all time scales from interannual to tidal, while bias and correlation metrics show that bottom temperatures in regions of commercial fishing activity in the Mid-Atlantic Bight and the Gulf of Maine are modeled well.},
	keywords = {Bottom temperature, Climatology, Coastal circulation, Data assimilation, Gulf of Maine, Mid Atlantic Bight, Numerical modeling, Sea level},
	issn = {0079-6611},
	doi = {10.1016/j.pocean.2022.102919},
	url = {https://www.sciencedirect.com/science/article/pii/S0079661122001781},
	author = {Wilkin, John and Levin, Julia and Moore, Andrew and Arango, Hernan and L{\'o}pez, Alexander and Hunter, Elias}
}
@article {RN84,
	title = {Surfzone State Estimation, with Applications to Quadcopter-Based Remote Sensing Data},
	journal = {Journal of Atmospheric and Oceanic Technology},
	volume = {35},
	number = {10},
	year = {2018},
	pages = {1881-1896},
	type = {Journal Article},
	abstract = {A one-dimensional variational data assimilation (1DVar) method is presented based on the depth- and time-averaged alongshore-uniform surfzone wave and current equations, for simultaneous estimation of three uncertain variables: bathymetry, incident wave boundary conditions, and bed roughness. The method is validated using twin tests and in situ field observations, and its results are shown to be comparable to those of an existing ensemble-based bathymetry inversion technique. Unlike existing techniques, the ability to simultaneously estimate boundary conditions and bed roughness along with bathymetry also means the 1DVar method can produce full state estimates without the requirement for additional supporting measurements (e.g., direct measurements of the incident waves). A proof-of-concept field application is shown using observations collected from an unmanned quadcopter sensor package that measures surfzone wave height from a fixed-beam lidar range finder, and time-averaged longshore current from particle image velocimetry of drifting surface foam.},
	keywords = {Bayesian methods, Coastal flows, Data assimilation, Inverse methods, Numerical analysis/modeling, Variational analysis},
	issn = {0739-0572},
	doi = {10.1175/JTECH-D-17-0205.1},
	url = {https://doi.org/10.1175/JTECH-D-17-0205.1},
	author = {Wilson, Gregory and Berezhnoy, Stephen}
}
