Autonomous four-dimensional mapping and tracking of a coastal upwelling front by an autonomous underwater vehicle

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Field Robotics 33 (2016): 67-81, doi:10.1002/rob.21617Coastal upwelling is a wind-driven ocean process that brings cooler, saltier, and nutrient-rich deep water upward to the surface. The boundary between the upwelling water and the normally stratified water is called the “upwelling front.” Upwelling fronts support enriched phytoplankton and zooplankton populations, thus they have great influences on ocean ecosystems. Traditional ship-based methods for detecting and sampling ocean fronts are often laborious and very difficult, and long-term tracking of such dynamic features is practically impossible. In our prior work, we developed a method of using an autonomous underwater vehicle (AUV) to autonomously detect an upwelling front and track the front's movement on a fixed latitude, and we applied the method in scientific experiments. In this paper, we present an extension of the method. Each time the AUV crosses and detects the front, the vehicle makes a turn at an oblique angle to recross the front, thus zigzagging through the front to map the frontal zone. The AUV's zigzag tracks alternate in northward and southward sweeps, so as to track the front as it moves over time. This way, the AUV maps and tracks the front in four dimensions—vertical, cross-front, along-front, and time. From May 29 to June 4, 2013, the Tethys long-range AUV ran the algorithm to map and track an upwelling front in Monterey Bay, CA, over five and one-half days. The tracking revealed spatial and temporal variabilities of the upwelling front.This work was supported by the David and Lucile Packard Foundation

Evolution of a physical and biological front from upwelling to relaxation

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Continental Shelf Research 108 (2015): 55-64, doi:10.1016/j.csr.2015.08.005.Fronts influence the structure and function of coastal marine ecosystems. Due to the complexity and dynamic nature of coastal environments and the small scales of frontal gradient zones, frontal research is difficult. To advance this challenging research we developed a method enabling an autonomous underwater vehicle (AUV) to detect and track fronts, thereby providing high-resolution observations in the moving reference frame of the front itself. This novel method was applied to studying the evolution of a frontal zone in the coastal upwelling environment of Monterey Bay, California, through a period of variability in upwelling intensity. Through 23 frontal crossings in four days, the AUV detected the front using real-time analysis of vertical thermal stratification to identify water types and the front between them, and the vehicle tracked the front as it moved more than 10 km offshore. The physical front coincided with a biological front between strongly stratified phytoplankton-enriched water inshore of the front, and weakly stratified phytoplankton-poor water offshore of the front. While stratification remained a consistent identifier, conditions on both sides of the front changed rapidly as regional circulation responded to relaxation of upwelling winds. The offshore water type transitioned from relatively cold and saline upwelled water to relatively warm and fresh coastal transition zone water. The inshore water type exhibited an order of magnitude increase in chlorophyll concentrations and an associated increase in oxygen and decrease in nitrate. It also warmed and freshened near the front, consistent with the cross-frontal exchange that was detected in the high-resolution AUV data. AUV-observed cross-frontal exchanges beneath the surface manifestation of the front emphasize the importance of AUV synoptic water column surveys in the frontal zone.This work was supported by the David and Lucile Packard Foundation

A Comparison of the Pac-X Trans-Pacific Wave Glider Data and Satellite Data (MODIS, Aquarius, TRMM and VIIRS)

Tracy A. Villareal, Marine Science Institute and Department of Marine Science, The University of Texas at Austin, Port Aransas, Texas, United States of AmericaCara Wilson, Environmental Research Division, Southwest Fisheries Science Center, National Marine Fisheries Service, NOAA, Pacific Grove, California, United States of AmericaFour wave-propelled autonomous vehicles (Wave Gliders) instrumented with a variety of oceanographic and meteorological sensors were launched from San Francisco, CA in November 2011 for a trans-Pacific (Pac-X) voyage to test platform endurance. Two arrived in Australia, one in Dec 2012 and one in February 2013, while the two destined for Japan both ran into technical difficulties and did not arrive at their destination. The gliders were all equipped with sensors to measure temperature, salinity, turbidity, oxygen, and both chlorophyll and oil fluorescence. Here we conduct an initial assessment of the data set, noting necessary quality control steps and instrument utility. We conduct a validation of the Pac-X dataset by comparing the glider data to equivalent, or near-equivalent, satellite measurements. Sea surface temperature and salinity compared well to satellite measurements. Chl fluorescence from the gliders was more poorly correlated, with substantial between glider variability. Both turbidity and oil CDOM sensors were compromised to some degree by interfering processes. The well-known diel cycle in chlorophyll fluorescence was observed suggesting that mapping physiological data over large scales is possible. The gliders captured the Pacific Ocean’s major oceanographic features including the increased chlorophyll biomass of the California Current and equatorial upwelling. A comparison of satellite sea surface salinity (Aquarius) and glider-measured salinity revealed thin low salinity lenses in the southwestern Pacific Ocean. One glider survived a direct passage through a tropical cyclone. Two gliders traversed an open ocean phytoplankton bloom; extensive spiking in the chlorophyll fluorescence data is consistent with aggregation and highlights another potential future use for the gliders. On long missions, redundant instrumentation would aid in interpreting unusual data streams, as well as a means to periodically image the sensor heads. Instrument placement is critical to minimize bubble-related problems in the data.The authors have no support or funding to report.Marine ScienceEmail: [email protected]

Phytoplankton Hotspot Prediction With an Unsupervised Spatial Community Model

Many interesting natural phenomena are sparsely distributed and discrete. Locating the hotspots of such sparsely distributed phenomena is often difficult because their density gradient is likely to be very noisy. We present a novel approach to this search problem, where we model the co-occurrence relations between a robot's observations with a Bayesian nonparametric topic model. This approach makes it possible to produce a robust estimate of the spatial distribution of the target, even in the absence of direct target observations. We apply the proposed approach to the problem of finding the spatial locations of the hotspots of a specific phytoplankton taxon in the ocean. We use classified image data from Imaging FlowCytobot (IFCB), which automatically measures individual microscopic cells and colonies of cells. Given these individual taxon-specific observations, we learn a phytoplankton community model that characterizes the co-occurrence relations between taxa. We present experiments with simulated robot missions drawn from real observation data collected during a research cruise traversing the US Atlantic coast. Our results show that the proposed approach outperforms nearest neighbor and k-means based methods for predicting the spatial distribution of hotspots from in-situ observations.Comment: To appear in ICRA 2017, Singapor

Autonomous sampling of ocean submesoscale fronts with ocean gliders and numerical model forecasting

Submesoscale fronts arising from mesoscale stirring are ubiquitous in the ocean and have a strong impact on upper-ocean dynamics. This work presents a method for optimizing the sampling of ocean fronts with autonomous vehicles at meso- and submesoscales, based on a combination of numerical forecast and autonomous planning. This method uses a 48-h forecast from a real-time high-resolution data-assimilative primitive equation ocean model, feature detection techniques, and a planner that controls the observing platform. The method is tested in Monterey Bay, off the coast of California, during a 9-day experiment focused on sampling subsurface thermohaline-compensated structures using a Seaglider as the ocean observing platform. Based on model estimations, the sampling “gain,” defined as the magnitude of isopycnal tracer variability sampled, is 50% larger in the feature-chasing case with respect to a non-feature-tracking scenario. The ability of the model to reproduce, in space and time, thermohaline submesoscale features is evaluated by quantitatively comparing the model and glider results. The model reproduces the vertical (~50–200 m thick) and lateral (~5–20 km) scales of subsurface subducting fronts and near-bottom features observed in the glider data. The differences between model and glider data are, in part, attributed to the selected glider optimal interpolation parameters and to uncertainties in the forecasting of the location of the structures. This method can be exported to any place in the ocean where high-resolution data-assimilative model output is available, and it allows for the incorporation of multiple observing platforms

Autonomous sampling of ocean submesoscale fronts with ocean gliders and numerical model forecasting

Submesoscale fronts arising from mesoscale stirring are ubiquitous in the ocean and have a strong impact on upper-ocean dynamics. This work presents a method for optimizing the sampling of ocean fronts with autonomous vehicles at meso- and submesoscales, based on a combination of numerical forecast and autonomous planning. This method uses a 48-h forecast from a real-time high-resolution data-assimilative primitive equation ocean model, feature detection techniques, and a planner that controls the observing platform. The method is tested in Monterey Bay, off the coast of California, during a 9-day experiment focused on sampling subsurface thermohaline-compensated structures using a Seaglider as the ocean observing platform. Based on model estimations, the sampling “gain,” defined as the magnitude of isopycnal tracer variability sampled, is 50% larger in the feature-chasing case with respect to a non-feature-tracking scenario. The ability of the model to reproduce, in space and time, thermohaline submesoscale features is evaluated by quantitatively comparing the model and glider results. The model reproduces the vertical (~50–200 m thick) and lateral (~5–20 km) scales of subsurface subducting fronts and near-bottom features observed in the glider data. The differences between model and glider data are, in part, attributed to the selected glider optimal interpolation parameters and to uncertainties in the forecasting of the location of the structures. This method can be exported to any place in the ocean where high-resolution data-assimilative model output is available, and it allows for the incorporation of multiple observing platforms

Autonomous tracking of salinity-intrusion fronts by a long-range autonomous underwater vehicle

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Zhang, Y., Yoder, N., Kieft, B., Kukulya, A., Hobson, B. W., Ryan, S., & Gawarkiewicz, G. G. Autonomous tracking of salinity-intrusion fronts by a long-range autonomous underwater vehicle. Ieee Journal of Oceanic Engineering, (2022): 1-9, https://doi.org/10.1109/JOE.2022.3146584.Shoreward intrusions of anomalously salty water along the continental shelf of the Middle Atlantic Bight are often observed in spring and summer. Exchange of heat, nutrients, and carbon across the salinity-intrusion front has a significant impact on the marine ecosystem and fisheries. In this article, we developed a method of using an autonomous underwater vehicle (AUV) to detect a salinity-intrusion front and track the front's movement. Autonomous front detection is based on the different vertical structures of salinity in the two distinct water types: the vertical difference of salinity is large in the intruding saltier water because of the salinity “tongue” at mid-depth, but is small in the nearshore fresher water due to absence of the salinity anomaly. Every time the AUV crosses and detects the front, the vehicle makes a turn at an oblique angle to cross the front, thus zigzagging through the front to map the frontal zone. The AUV's zigzags sweep back and forth to track the front as it moves over time. From June 25 to 30, 2021, a Tethys-class long-range AUV mapped and tracked a salinity-intrusion front on the southern New England shelf. The frontal tracking revealed the salinity intrusion's 3-D structure and temporal evolution with unprecedented detail.This work was supported in part by the National Science Foundation under Grant OCE-1851261 and in part by the David and Lucile Packard Foundation

Autonomous & adaptive oceanographic front tracking on board autonomous underwater vehicles

Oceanic fronts, similar to atmospheric fronts, occur at the interface of two fluid (water) masses of varying characteristics. In regions such as these where there are quantifiable physical, chemical, or biological changes in the ocean environment, it is possible - with the proper instrumentation - to track, or map, the front boundary. In this paper, the front is approximated as an isotherm that is tracked autonomously and adaptively in 2D (horizontal) and 3D space by an autonomous underwater vehicle (AUV) running MOOS-IvP autonomy. The basic, 2D (constant depth) front tracking method developed in this work has three phases: detection, classification, and tracking, and results in the AUV tracing a zigzag path along and across the front. The 3D AUV front tracking method presented here results in a helical motion around a central axis that is aligned along the front in the horizontal plane, tracing a 3D path that resembles a slinky stretched out along the front. To test and evaluate these front tracking methods (implemented as autonomy behaviors), virtual experiments were conducted with simulated AUVs in a spatiotemporally dynamic MIT MSEAS ocean model environment of the Mid-Atlantic Bight region, where a distinct temperature front is present along the shelfbreak. A number of performance metrics were developed to evaluate the performance of the AUVs running these front tracking behaviors, and the results are presented herein.United States. Office of Naval Research (Awards N00014-11-1-0097 and N00014-14-1-0214