@article {niebergall_evaluation_2023,
	title = {Evaluation of new and net community production estimates by multiple ship-based and autonomous observations in the Northeast Pacific Ocean},
	journal = {Elementa: Science of the Anthropocene},
	volume = {11},
	number = {1},
	year = {2023},
	note = {Publisher: University of California Press},
	abstract = {New production (NP) and net community production (NCP) measurements are often used as estimates of carbon export potential from the mixed layer of the ocean, an important process in the regulation of global climate. Diverse methods can be used to measure NP and NCP, from research vessels, autonomous platforms, and remote sensing, each with its own set of benefits and uncertainties. The various methods are rarely applied simultaneously in a single location, limiting our ability for direct comparisons of the resulting measurements. In this study, we evaluated NP and NCP from thirteen independent datasets collected via in situ, in vitro, and satellite-based methods near Ocean Station Papa during the 2018 Northeast Pacific field campaign of the NASA project EXport Processes in the Ocean from RemoTe Sensing (EXPORTS). Altogether, the datasets indicate that carbon export potential was relatively low (median daily averages between -5.1 and 12.6 mmol C m-2 d-1), with most measurements indicating slight net autotrophy in the region. This result is consistent with NCP estimates based on satellite measurements of sea surface temperature and chlorophyll a. We explored possible causes of discrepancies among methods, including differences in assumptions about stoichiometry, vertical integration, total volume sampled, and the spatiotemporal extent considered. Results of a generalized additive mixed model indicate that the spatial variation across platforms can explain much of the difference among methods. Once spatial variation and temporal autocorrelation are considered, a variety of methods can provide consistent estimates of NP and NCP, leveraging the strengths of each approach.},
	keywords = {14 Life Below Water},
	issn = {2785-4558},
	doi = {10.1525/elementa.2021.00107},
	author = {Niebergall, AK and Traylor, S and Huang, Y and Feen, M and Meyer, MG and McNair, HM and Nicholson, D and Fassbender, AJ and Omand, MM and Marchetti, A and Menden-Deuer, S and Tang, W and Gong, W and Tortell, P and Hamme, R and Cassar, N}
}
@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 {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 {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.}
}
