@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 {RN242,
	title = {The impact of remote sensing observations on cross-shelf transport estimates from 4D-Var analyses of the Mid-Atlantic Bight},
	journal = {Advances in Space Research},
	volume = {68},
	number = {2},
	year = {2021},
	pages = {553-570},
	type = {Journal Article},
	abstract = {This paper explores the impact of the individual components of a coastal ocean observing system on estimates of the circulation derived from a state-of-the-art analysis and forecast system for the Mid-Atlantic Bight and Gulf of Maine. The foundation of these activities is the Regional Ocean Modeling System 4-dimensional variational (4D-Var) data assimilation platform, which is run in support of the Mid-Atlantic Regional Association Coastal Ocean Observing System as part of the U.S. Integrated Ocean Observing System. The specific focus of this study is on the impact of remote sensing observations from both space- and land-based platforms on estimates of cross-shelf transport in the vicinity of the National Science Foundation Ocean Observatories Initiative Pioneer array. Sea surface temperature (SST) and sea surface height (SSH) were found to have, on average, a similar impact on the transport estimates. However, during a typical 3-day 4D-Var assimilation cycle, approximately two orders of magnitude more observations of SST than SSH are used in the model, and closer analysis shows that each altimeter measurement has approximately 50 times more impact on the transport estimates than an individual SST observation. This highlights the value of altimetry data for ocean state estimation, and the significance of expanding the altimeter constellation. The observations that are most impactful of all are in situ measurements of temperature and salinity, which have typically 3{\textendash}4 times more impact than an individual SSH datum. A robust geographical distribution of the observation impacts emerges across a range of transport metrics which results from the combined influence of space-time dynamical interpolation and error covariance information within the 4D-Var system. The observation impact calculations suggest that High Frequency (HF) radar estimates of surface currents have relatively little direct influence on cross-shelf transport estimates. However, quantification of the sensitivity of these same estimates to changes in the observing system indicate that HF radar observations indirectly provide important information. This is understood in the current system by appealing to the idea of borrowing strength from the field of statistics in which some observations (satellite remote sensing in the case considered here) can borrow strength from other, seemingly less important observations.},
	keywords = {4D-Var, Mid Atlantic Bight, Observation impact},
	issn = {0273-1177},
	doi = {10.1016/j.asr.2019.09.012},
	author = {Levin, J. and Arango, H. G. and Laughlin, B. and Wilkin, J. and Moore, A. M.}
}
