@article {stevens_ocean_2023,
	title = {Ocean Ensemble-Enabled Stochastic Acoustic Prediction With Operational Metrics: New England Shelf Break Acoustics Signals and Noise Experiment},
	journal = {IEEE Journal of Oceanic Engineering},
	year = {2023},
	note = {Conference Name: IEEE Journal of Oceanic Engineering},
	pages = {1{\textendash}28},
	abstract = {This article describes the results of the New England shelf break acoustics (NESBA) experiment as they pertain to acoustic prediction and the quantification of associated uncertainties in relevant operational metrics. The uncertainties considered here are those due to the imperfect sensing of the water column, ambient noise (AN), and the seabed, and the impact this has on ocean forecasting and acoustic performance prediction accuracy. Operational metrics are designed to provide an acoustic system operator with actionable guidance relating to likely mean source detection ranges and associated uncertainties with specific guidance on the degree to which specific environmental factors (e.g., oceanography, seabed, and ambient noise) contribute to the predicted uncertainty levels. High-resolution regional Navy Coastal Ocean Model (NCOM) ensemble forecasts were generated to capture oceanographic variability and uncertainty. Passive ambient noise-based seabed measurements were conducted to estimate seabed properties. Extensive AN and conductivity, temperature, and depth (CTD) measurements were also conducted. Measurement-based versus model-based acoustic prediction metrics are compared as an initial validation of the underlying methodology. It is shown that Global Hybrid Coordinate Ocean Model (HYCOM) ocean forecasts with databased AN and seabed parameters result in very large metric uncertainties, while leveraging high-resolution NCOM with ensembles and in situ AN and seabed measurements can result in substantially reduced uncertainties. It is also demonstrated that improved ocean modeling and sensing can be leveraged to determine the best receiver depth, associated uncertainty levels, and uncertainty drivers. An operational concept for generating acoustic prediction metrics and associated operator environmental sensing guidance is proposed.},
	keywords = {acoustics, Cumulative probability of detection, Measurement, passive sonar, Predictive models, Sea measurements, Sea surface, stochastic acoustic prediction, Temperature measurement, uncertainty},
	issn = {1558-1691},
	doi = {10.1109/JOE.2023.3267788},
	author = {Stevens, William K. and Siderius, Martin and Carrier, Matthew J. and Lin, Ying-Tsong and Wendeborn, Drew}
}
@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}
}
