@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 {RN85,
	title = {Assessing Coastal SMAP Surface Salinity Accuracy and Its Application to Monitoring Gulf of Maine Circulation Dynamics},
	journal = {Remote Sensing},
	volume = {10},
	number = {8},
	year = {2018},
	type = {Journal Article},
	abstract = {Monitoring the cold and productive waters of the Gulf of Maine and their interactions with the nearby northwestern (NW) Atlantic shelf is important but challenging. Although remotely sensed sea surface temperature (SST), ocean color, and sea level have become routine, much of the water exchange physics is reflected in salinity fields. The recent invention of satellite salinity sensors, including the Soil Moisture Active Passive (SMAP) radiometer, opens new prospects in regional shelf studies. However, local sea surface salinity (SSS) retrieval is challenging due to both cold SST limiting salinity sensor sensitivity and proximity to land. For the NW Atlantic, our analysis shows that SMAP SSS is subject to an SST-dependent bias that is negative and amplifies in winter and early spring due to the SST-related drop in SMAP sensor sensitivity. On top of that, SMAP SSS is subject to a land contamination bias. The latter bias becomes noticeable and negative when the antenna land contamination factor (LC) exceeds 0.2\%, and attains maximum negative values at LC = 0.4\%. Coastward of LC = 0.5\%, a significant positive land contamination bias in absolute SMAP SSS is evident. SST and land contamination bias components are seasonally dependent due to seasonal changes in SST/winds and terrestrial microwave properties. Fortunately, it is shown that SSS anomalies computed relative to a satellite SSS climatology can effectively remove such seasonal biases along with the real seasonal cycle. SMAP monthly SSS anomalies have sufficient accuracy and applicability to extend nearer to the coasts. They are used to examine the Gulf of Maine water inflow, which displayed important water intrusions in between Georges Banks and Nova Scotia in the winters of 2016/17 and 2017/18. Water intrusion patterns observed by SMAP are generally consistent with independent measurements from the European Soil Moisture Ocean Salinity (SMOS) mission. Circulation dynamics related to the 2016/2017 period and enhanced wind-driven Scotian Shelf transport into the Gulf of Maine are discussed.
},
	keywords = {bias characteristics, Gulf of Maine, satellite salinity, Scotian Shelf},
	issn = {2072-4292},
	doi = {10.3390/rs10081232},
	author = {Grodsky, A. Semyon and Vandemark, Douglas and Feng, Hui}
}
@article {RN81,
	title = {Satellite detection of an unusual intrusion of salty slope water into a marginal sea: Using SMAP to monitor Gulf of Maine inflows},
	journal = {Remote Sensing of Environment},
	volume = {217},
	year = {2018},
	pages = {550-561},
	type = {Journal Article},
	abstract = {Satellite salinity from the Soil Moisture Active Passive (SMAP) mission and in situ observations are used to diagnose the source of a significant increase in warm and salty surface water entering the Gulf of Maine (GoM) in the winter of 2017{\textendash}2018. SMAP salinity anomaly data indicate that this event was related to a salty feature that moved along the northwestern Atlantic shelf break from near the Grand Banks southwest towards the GoM over eight months before entering the Gulf in December 2017 to January 2018. Satellite altimetry, sea surface salinity, and sea surface temperature data suggest that, before entering the GoM, the salty feature interacted with Gulf Stream meanders and eddies several times, helping to sustain the water mass. It is likely that feature interactions with a warm Gulf Stream meander took place in the fall of 2017, helping to advect high salinity water onto the shelf and then into the GoM as a surface trapped feature in late fall 2017. According to satellite salinity data, this episode led to significant salinification (about 1 psu) in the northeastern GoM. Interior GoM buoy salinity data agree in showing four months of increased GoM salinity of the upper 50 m starting in November 2017. Buoy T/S analyses characterize this surface inflow as modified warm Atlantic slope water, typically seen only below 100 m and previously unobserved at the surface in the 15-year buoy record. This new salty water circulated cyclonically along the GoM coastline and mixed into the deeper Gulf through February 2018. Its intrusion may have also enhanced the cyclonic winter circulation in the Gulf. GoM surface salinity anomalies ended abruptly in early March 2018 coincident with the occurrence of a bomb cyclone and its associated strong upper ocean mixing.

},
	keywords = {Gulf of Maine, satellite salinity, Sea level, Temperature},
	issn = {0034-4257},
	doi = {10.1016/j.rse.2018.09.004},
	url = {http://www.sciencedirect.com/science/article/pii/S0034425718304152},
	author = {Grodsky, Semyon A. and Vandemark, Douglas and Feng, Hui and Levin, Julia}
}
