Scientific Papers

Abstract: Saildrones are unmanned surface vehicles engineered for oceanographic research and powered by wind and solar energy. In the summer of 2016, two Saildrones surveyed the southeastern Bering Sea using passive acoustics to listen for vocalizations of marine mammals and active acoustics to quantify the spatial distribution of small and large fishes. Fish distributions were examined during foraging trips of northern fur seals (Callorhinus ursinus), and initial results suggest these prey distributions may influence the diving behavior of fur seals. The Saildrone is faster, has greater instrument capacity, and requires less support services than its counterparts. This innovative platform performed well in stormy conditions, and it demonstrated the potential to augment fishery surveys and advance ecosystem research.

Abstract: Persistence of oil floating in the ocean is an important factor for evaluating hydrocarbon fluxes from natural seeps and anthropogenic releases into the environment. The objective of this work is to estimate the surface residence-time of the oil slick and to determine the importance of wind and surface currents on the trajectory and fate of the released oil. Oil slicks released from natural hydrocarbon seeps located in Green Canyon 600 lease block and its surrounding region in the Gulf of Mexico were analyzed. A Texture Classifying Neural Network Algorithm was used to delineate georectified polygons for oil slicks from 41 synthetic aperture radar images. Trajectories of the oil slicks were investigated by employing a Lagrangian particle-tracking surface oil drift model. A set of numerical simulations was performed by increasing hindcast interval in reverse time order from the image collection time in order to obtain the closest resemblance between the simulated oil pathways and the length and shape of the oil slicks observed in SAR images. The average surface residence-time predicted from the hindcast modeling was 6.4 h (± 5.7 h). Analysis of a linear regression model, including observed oil slick lengths and variables of wind, surface current, and their relative direction, indicated a statistically significant negative effect of wind speed on the surface oil drift. Higher wind speed (> 7 m s-1) reduced length of the oil slicks. When wind and surface currents were driving forces of the surface oil drift model, a good agreement between simulated trajectories and subsequent satellite observations (R2 = 0.9) suggested that a wind scaling coefficient of 0.035 and a wind deflection angle of 20º to the right of the wind direction were acceptable approximations for modeling wind effects in this study. Results from the numerical experimentation were supported by in situ observations conducted by a wind-powered autonomous surface vehicle (Saildrone). Results indicated that the surface currents are, indeed, responsible for stretching oil slicks and that surface winds are largely responsible for the disappearance of the oil slicks from the sea surface. Under conditions of low wind and strong current, natural seeps can produce oil slicks that are longer than 20 km and persist for up to 48 h.

Abstract: Demonstrating the feasibility of operating the Saildrone in the northern Gulf of Mexico within a high amount of maritime activity, including commercial and recreational fishing, shipping, and oil and gas platforms and associated servicing vessels. Demonstrate the utility of “high-speed” (up to 9 knots) wind-propelled surface vehicles as fast adaptive sampling response tools and to effectively fill in gaps between moorings at separations greater than the local correlation length scales; Collect a dataset that can be used for regional ecosystem model development and for designing the observational systems needed for process studies of shelf-ocean exchange phenomena of import to the carbon cycle in the Gulf.

Abstract: During recent decades the US Arctic is experiencing a rapid loss of sea ice and subsequently increasingly warmer water temperatures. To better study this economically and culturally important marine ecosystem and the changes that are occurring, the use of new technologies is being explored to supplement traditional ship, satellite and mooring based data collection techniques. Unmanned surface vehicles (USV) are a rapidly advancing technology that has the potential to meet the requirement for long duration and economical scientific data collection with the ability for real-time data and adaptive sampling. In 2015, the National Oceanic and Atmospheric Administration's Pacific Marine Environmental Laboratory (NOAA-PMEL), the University of Washington (UW) and Saildrone Inc. (Alameda, California) explored the use of a novel USV technology in the Bering Sea and Norton Sound. Two Saildrones, wind and solar powered unmanned surface vehicles that can be used for extended research missions in challenging environments, were equipped with a suite of meteorological and oceanographic sensors. During the >3 month mission, the vehicles each traveled over 4100 nm, successfully completing several scientific survey assignments. This mission demonstrated the capability of the Saildrone vehicle to be launched from a dock to conduct autonomous and adaptive oceanographic research in a harsh, high-latitude environment.

Abstract: New technologies can help scientists measure and understand Arctic warming, sea ice loss and ecosystem change. NOAA has worked with Saildrone, Inc., to develop an unmanned surface vehicle (USV)—Saildrone—to make ocean surface measurements autonomously, even in challenging high-latitude conditions. USVs augment traditional research ship cruises, mitigate ship risk in high seas and shallow water, and make lower cost measurements. Under remote control, USV sampling strategy can be adapted to meet changing needs. Two Saildrones conducted 97-day missions in the Bering Sea in spring-summer 2015, reliably measuring atmospheric and oceanic parameters. Measurements were validated against shipboard values. Following that, the Saildrone sampling strategies were modified, first to measure the effects of sea-ice melt on surface cooling and freshening, and then to study the Yukon River plume.

In 2021, a novel NOAA-Saildrone project deployed five uncrewed surface vehicle Saildrones (SDs) to monitor regions of the Atlantic Ocean and Caribbean Sea frequented by tropical cyclones. One of the SDs, SD-1045, crossed Hurricane Sam (Category 4) on September 30, providing the first-ever surface-ocean videos of conditions in the core of a major hurricane and reporting near-surface winds as high as 40 m/s. Here, we present a comprehensive analysis and interpretation of the Saildrone ocean surface wind measurements in Hurricane Sam, using the following datasets for direct and indirect comparisons: an NDBC buoy in the path of the storm, radiometer tropical cyclone (TC) winds from SMAP and AMSR2, wind retrievals from the ASCAT scatterometers and SAR (RadarSat2), and HWRF model winds. The SD winds show excellent consistency with the satellite observations and a remarkable ability to detect the strength of the winds at the SD location. We use the HWRF model and satellite data to perform cross-comparisons of the SD with the buoy, which sampled different relative locations within the storm. Finally, we review the collective consistency among these measurements by describing the uncertainty of each wind dataset and discussing potential sources of systematic errors, such as the impact of extreme conditions on the SD measurements and uncertainties in the methodology.