@article {artana_intense_2023,
	title = {Intense anticyclones at the global Argentine Basin array of the Ocean Observatory Initiative},
	journal = {Ocean Science},
	volume = {19},
	number = {3},
	year = {2023},
	note = {Publisher: Copernicus GmbH},
	month = {jun},
	pages = {953{\textendash}971},
	abstract = {We analyzed physical oceanic parameters gathered by a mooring array at mesoscale spatial sampling deployed in the Argentine Basin within the Ocean Observatory Initiative, a National Science Foundation major research facility. The array was maintained at 42o S and 42o W, a historically sparsely sampled region with small ocean variability, over 34 months from March 2015 to January 2018. The data documented four anticyclonic extreme-structure events in 2016. The four anticyclonic structures had different characteristics (size, vertical extension, origin, lifetime and Rossby number). They all featured near-inertial waves (NIWs) trapped at depth and low Richardson values well below the mixed layer. Low Richardson values suggest favorable conditions for mixing. The anticyclonic features likely act as mixing structures at the pycnocline, bringing heat and salt from the South Atlantic Central Water to the Antarctic Intermediate Waters. The intense structures were unique in the 29-year-long satellite altimetry record at the mooring site. The Argentine Basin is populated with many anticyclones, and mixing associated with trapped NIWs probably plays an important role in setting up the upper-water-mass characteristics in the basin.},
	issn = {1812-0784},
	doi = {10.5194/os-19-953-2023},
	url = {https://os.copernicus.org/articles/19/953/2023/},
	author = {Artana, Camila and Provost, Christine}
}
@article {RN177,
	title = {Deep Learning for Predicting Significant Wave Height From Synthetic Aperture Radar},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {PP},
	number = {99},
	year = {2020},
	pages = {1-9},
	type = {Journal Article},
	abstract = {The Sentinel-1 satellites equipped with synthetic aperture radars (SARs) provide near-global coverage of the world{\textquoteright}s oceans every six days. We curate a data set of collocations between SAR and altimeter satellites and investigate the use of deep learning to predict significant wave height from SAR. While previous models for predicting geophysical quantities from SAR rely heavily on feature-engineering, our approach learns directly from low-level image cross-spectra. Training on collocations from 2015 to 2017, we demonstrate on test data from 2018 that deep learning reduces the state-of-the-art root mean squared error by 50\%, from 0.6 to 0.3 m when compared to altimeter data. Furthermore, we isolate the contributions of different features to the model performance.},
	keywords = {Data models, Modulation, Satellites, Sea measurements, Sea surface, Synthetic aperture radar},
	doi = {10.1109/tgrs.2020.3003839},
	url = {https://app.dimensions.ai/details/publication/pub.1129457664},
	author = {Quach, Brandon and Glaser, Yannik and Stopa, Justin Edward and Mouche, Alexis Aurelien and Sadowski, Peter}
}
@article {RN70,
	title = {Calibration of the Normalized Radar Cross Section for Sentinel-1 Wave Mode},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {57},
	number = {3},
	year = {2019},
	pages = {1514-1522},
	type = {Journal Article},
	abstract = {Sentinel-1 (S-1) is a two-satellite constellation for continuity of operational synthetic aperture radar (SAR) observations. Wave mode (WV) is the default mode over open ocean for S-1 to monitor global ocean waves and wind field. Therefore, proper radiometric calibration is essential to accurately infer these geophysical quantities. Based on the global data set acquired by S-1A WV, assessment of normalized radar cross section (NRCS) is carried out through comparison with CMOD5.N predictions over open ocean. The calibration accuracy quantified by NRCS residuals between SAR measurements and CMOD5.N demonstrates distinct features for two incidence angles (23.8{\textdegree} and 36.8{\textdegree}). Particularly, NRCS at 23.8{\textdegree} is overall consistent with CMOD5.N, while NRCS at 36.8{\textdegree} displays great deviation. Two recalibration methods are then implemented by examining the backscattering profile over Amazon rain forest and ocean calibration. Both methods show the necessity for recalibration and obtain comparable correction factors for WV1 and WV2, respectively. The NRCS residuals by applying both methods are significantly reduced toward zero. By comparison, ocean calibration is more efficient and practical to implement.},
	keywords = {Calibration, Histograms, Oceans, Radiometry, Rain, Synthetic aperture radar, Wind speed},
	issn = {0196-2892},
	doi = {10.1109/TGRS.2018.2867035},
	author = {Li, H. and Mouche, A. and Stopa, J. E. and Chapron, B.}
}
@article {RN108,
	title = {Integrated Observations of Global Surface Winds, Currents, and Waves: Requirements and Challenges for the Next Decade},
	journal = {Frontiers in Marine Science},
	volume = {6},
	year = {2019},
	pages = {425},
	type = {Journal Article},
	abstract = {Ocean surface winds, currents, and waves play a crucial role in exchanges of momentum, energy, heat, freshwater, gases, and other tracers between the ocean, atmosphere, and ice. Despite surface waves being strongly coupled to the upper ocean circulation and the overlying atmosphere, efforts to improve ocean, atmospheric, and wave observations and models have evolved somewhat independently. From an observational point of view, community efforts to bridge this gap have led to proposals for satellite Doppler oceanography mission concepts, which could provide unprecedented measurements of absolute surface velocity and directional wave spectrum at global scales. This paper reviews the present state of observations of surface winds, currents, and waves, and it outlines observational gaps that limit our current understanding of coupled processes that happen at the air-sea-ice interface. A significant challenge for the coming decade of wind, current, and wave observations will come in combining and interpreting measurements from (a) wave-buoys and high-frequency radars in coastal regions, (b) surface drifters and wave-enabled drifters in the open-ocean, marginal ice zones, and wave-current interaction {\textquotedblleft}hot-spots,{\textquotedblright} and (c) simultaneous measurements of absolute surface currents, ocean surface wind vector, and directional wave spectrum from Doppler satellite sensors.},
	keywords = {absolute surface velocity, air-sea interactions, Doppler oceanography from space, ocean surface winds, surface waves},
	issn = {2296-7745},
	doi = {10.3389/fmars.2019.00425},
	url = {https://www.frontiersin.org/article/10.3389/fmars.2019.00425},
	author = {Villas B{\^o}as, Ana B. and Ardhuin, Fabrice and Ayet, Alex and Bourassa, Mark A. and Brandt, Peter and Chapron, Betrand and Cornuelle, Bruce D. and Farrar, J. T. and Fewings, Melanie R. and Fox-Kemper, Baylor and Gille, Sarah T. and Gommenginger, Christine and Heimbach, Patrick and Hell, Momme C. and Li, Qing and Mazloff, Matthew R. and Merrifield, Sophia T. and Mouche, Alexis and Rio, Marie H. and Rodriguez, Ernesto and Shutler, Jamie D. and Subramanian, Aneesh C. and Terrill, Eric J. and Tsamados, Michel and Ubelmann, Clement and van Sebille, Erik}
}
@article {RN32,
	title = {Detection of climate change-driven trends in phytoplankton phenology},
	journal = {Glob Chang Biol},
	volume = {24},
	number = {1},
	year = {2018},
	pages = {e101-e111},
	type = {Journal Article},
	abstract = {The timing of the annual phytoplankton spring bloom is likely to be altered in response to climate change. Quantifying that response has, however, been limited by the typically coarse temporal resolution (monthly) of global climate models. Here, we use higher resolution model output (maximum 5 days) to investigate how phytoplankton bloom timing changes in response to projected 21st century climate change, and how the temporal resolution of data influences the detection of long-term trends. We find that bloom timing generally shifts later at mid-latitudes and earlier at high and low latitudes by ~5 days per decade to 2100. The spatial patterns of bloom timing are similar in both low (monthly) and high (5 day) resolution data, although initiation dates are later at low resolution. The magnitude of the trends in bloom timing from 2006 to 2100 is very similar at high and low resolution, with the result that the number of years of data needed to detect a trend in phytoplankton phenology is relatively insensitive to data temporal resolution. We also investigate the influence of spatial scales on bloom timing and find that trends are generally more rapidly detectable after spatial averaging of data. Our results suggest that, if pinpointing the start date of the spring bloom is the priority, the highest possible temporal resolution data should be used. However, if the priority is detecting long-term trends in bloom timing, data at a temporal resolution of 20 days are likely to be sufficient. Furthermore, our results suggest that data sources which allow for spatial averaging will promote more rapid trend detection.},
	issn = {1365-2486 (Electronic) 1354-1013 (Linking)},
	doi = {10.1111/gcb.13886},
	url = {https://www.ncbi.nlm.nih.gov/pubmed/28871605},
	author = {Henson, S. A. and Cole, H. S. and Hopkins, J. and Martin, A. P. and Yool, A.}
}
@article {RN86,
	title = {Geostatistical Analysis of Mesoscale Spatial Variability and Error in SeaWiFS and MODIS/Aqua Global Ocean Color Data},
	journal = {Journal of Geophysical Research: Oceans},
	volume = {123},
	number = {1},
	year = {2018},
	pages = {22-39},
	type = {Journal Article},
	abstract = {Mesoscale (10{\textendash}300 km, weeks to months) physical variability strongly modulates the structure and dynamics of planktonic marine ecosystems via both turbulent advection and environmental impacts upon biological rates. Using structure function analysis (geostatistics), we quantify the mesoscale biological signals within global 13 year SeaWiFS (1998{\textendash}2010) and 8 year MODIS/Aqua (2003{\textendash}2010) chlorophyll a ocean color data (Level-3, 9 km resolution). We present geographical distributions, seasonality, and interannual variability of key geostatistical parameters: unresolved variability or noise, resolved variability, and spatial range. Resolved variability is nearly identical for both instruments, indicating that geostatistical techniques isolate a robust measure of biophysical mesoscale variability largely independent of measurement platform. In contrast, unresolved variability in MODIS/Aqua is substantially lower than in SeaWiFS, especially in oligotrophic waters where previous analysis identified a problem for the SeaWiFS instrument likely due to sensor noise characteristics. Both records exhibit a statistically significant relationship between resolved mesoscale variability and the low-pass filtered chlorophyll field horizontal gradient magnitude, consistent with physical stirring acting on large-scale gradient as an important factor supporting observed mesoscale variability. Comparable horizontal length scales for variability are found from tracer-based scaling arguments and geostatistical decorrelation. Regional variations between these length scales may reflect scale dependence of biological mechanisms that also create variability directly at the mesoscale, for example, enhanced net phytoplankton growth in coastal and frontal upwelling and convective mixing regions. Global estimates of mesoscale biophysical variability provide an improved basis for evaluating higher resolution, coupled ecosystem-ocean general circulation models, and data assimilation.},
	issn = {2169-9275},
	doi = {10.1002/2017JC013023},
	url = {https://doi.org/10.1002/2017JC013023},
	author = {Glover, David M. and Doney, Scott C. and Oestreich, William K. and Tullo, Alisdair W.}
}
@article {RN28,
	title = {A regime-dependent retrieval algorithm for near-surface air temperature and specific humidity from multi-microwave sensors},
	journal = {Remote Sensing of Environment},
	volume = {215},
	year = {2018},
	pages = {199-216},
	type = {Journal Article},
	abstract = {Near-surface specific humidity (qa) and air temperature (Ta) over the global ocean are important meteorological variables, but they cannot be retrieved directly from remote sensing. Many efforts have been made to develop algorithms that derive qa and Ta from multisensor microwave and/or infrared observations using in situ measurements as training datasets. However, uncertainty remains large in the resultant qa and Ta retrievals. In this study, 147 moored surface buoys are used to examine how buoy measured qa and Ta are related to satellite microwave brightness temperature (Tb) on the spatial scale from the warm/humid tropics to the cold/dry high latitudes. It is found that the Tb {\textendash} qa and Tb {\textendash} Ta relations are structured along two distinct, near-linear bands, with the primary band in the warm/humid regime and a secondary (weaker) band in the cold/dry regime. The step-like transition (or separation) between the two regimes occurs at 8{\textendash}10 g kg-1 for qa and 14{\textendash}17 {\textdegree}C for Ta. The evidence suggests that one algorithm may not be sufficient to extract qa and Ta from Tb in all regimes. Therefore, a high-latitude enhancement is added to the global algorithm so that the qa and Ta retrievals in the dry/cold regime can be specifically addressed. The new algorithms are applied to 11 microwave sensors, including SSM/I, SSMIS, and AMSU-A, from 1988 to 2016. Based on the 475,717 buoy collocations during the 29-year period, the retrieved qa and Ta have root-mean-square differences of 0.82 g kg-1 and 0.51 {\textdegree}C, respectively.

},
	keywords = {Microwave passive sensors, Moored surface buoys, Multisensor retrieval, Near-surface air temperature and specific humidity, Retrieval algorithm},
	issn = {00344257},
	doi = {10.1016/j.rse.2018.06.001},
	url = {https://staging-ddpp.dimensions.ai/details/publication/pub.1104605412},
	author = {Yu, Lisan and Jin, Xiangze}
}
@article {RN38,
	title = {Air-sea CO2 fluxes and the controls on ocean surface pCO(2) seasonal variability in the coastal and open-ocean southwestern Atlantic Ocean: a modeling study},
	journal = {Biogeosciences},
	volume = {12},
	number = {19},
	year = {2015},
	pages = {5793-5809},
	type = {Journal Article},
	abstract = {We use an eddy-resolving, regional ocean biogeochemical model to investigate the main variables and processes responsible for the climatological spatio-temporal variability of pCO2 and the air-sea CO2 fluxes in the southwestern Atlantic Ocean. Overall, the region acts as a sink of atmospheric CO2 south of 30{\textdegree} S, and is close to equilibrium with the atmospheric CO2 to the north. On the shelves, the ocean acts as a weak source of CO2, except for the mid/outer shelves of Patagonia, which act as sinks. In contrast, the inner shelves and the low latitude open ocean of the southwestern Atlantic represent source regions. Observed nearshore-to-offshore and meridional pCO2 gradients are well represented by our simulation. A sensitivity analysis shows the importance of the counteracting effects of temperature and dissolved inorganic carbon (DIC) in controlling the seasonal variability of pCO2. Biological production and solubility are the main processes regulating pCO2, with biological production being particularly important on the shelves. The role of mixing/stratification in modulating DIC, and therefore surface pCO2, is shown in a vertical profile at the location of the Ocean Observatories Initiative (OOI) site in the Argentine Basin (42{\textdegree} S, 42{\textdegree} W).},
	issn = {1726-4170},
	doi = {10.5194/bg-12-5793-2015},
	author = {Arruda, R. and Calil, P. H. R. and Bianchi, A. A. and Doney, S. C. and Gruber, N. and Lima, I. and Turi, G.}
}
