Information‐driven robotic sampling in the coastal ocean

Fossum, Trygve Olav; Eidsvik, Jo; Ellingsen, Ingrid; Alver, Morten Omholt; Fragoso, Glaucia Moreira; Johnsen, Geir; Mendes, Renato; Ludvigsen, Martin; Rajan, Kanna

doi:10.1002/rob.21805

Cited by 51 publications

(30 citation statements)

References 65 publications

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“…The AUV was deployed to autonomously collect high‐resolution data across the front using adaptive sampling; see, for example, Fossum et al (). Both front detection and sampling location were decided by a state‐based autonomous agent running onboard the AUV, optimizing data collection across and along the front.…”

Section: Datamentioning

confidence: 99%

Observations of Turbulence at a Near‐Surface Temperature Front in the Arctic Ocean

et al. 2020

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High-resolution ocean temperature, salinity, current, and turbulence data were collected at an Arctic thermohaline front in the Nansen Basin. The front was close to the sea ice edge and separated the cold and fresh surface melt water from the warm and saline mixed layer. Measurements were made on 18 September 2018, in the upper 100 m, from a research vessel and an autonomous underwater vehicle. Destabilizing surface buoyancy fluxes from a combination of heat loss to the atmosphere and cross-front Ekman transport by down-front winds reduced the potential vorticity in the upper ocean. Turbulence structure in the mixed layer was generally consistent with turbulence production through convection by heat loss to atmosphere and mechanical forcing by moderate winds. Conditions at the front were favorable for forced symmetric instability, a mechanism drawing energy from the frontal geostrophic current. A clear signature of increased dissipation from symmetric instability could not be identified; however, this instability could potentially account for the increased dissipation rates at the front location down to 40 m depth that could not be explained by the atmospheric forcing. This turbulence was associated with turbulent heat fluxes of up to 10 W m −2 , eroding the warm and cold intrusions observed between 30 and 60 m depth. A Seaglider sampled across a similar frontal structure in the same region 10 days after our survey. The submesoscale-to-turbulence-scale transitions and resulting mixing can be widespread and important in the Atlantic sector of the Arctic Ocean. Plain Language SummaryOn 18 September 2018, high-resolution temperature, salinity, current, and turbulence data were collected near a surface temperature and salinity front in the Nansen Basin north of Svalbard, within the framework of the Nansen Legacy Project. Measurements were performed from the research icebreaker Kronprins Haakon and using an autonomous underwater vehicle. The front separates warm and salty Atlantic-origin waters from cold and fresh Arctic-origin waters. Energetic turbulence in the upper 30 m was consistent with convection by heat loss to the atmosphere and mechanical forcing by moderate winds at the surface of the ocean. At the front, the conditions were favorable for forced symmetric instability, a mechanism drawing energy from the large-scale current. The contribution to turbulence from this instability could not be clearly identified but can be potentially important. Such observations linking 1-3 km scales to turbulence in the Arctic Ocean are rare. Turbulence at a front in the Arctic has consequences on the heat and nutrient transport from the slope to the deep Arctic Basin. A Seaglider sampled across a similar frontal structure in the same region end of September 2018. Observed frontal structure and mixing processes can be common in the Atlantic sector of the Arctic Ocean.

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Section: Datamentioning

confidence: 99%

Observations of Turbulence at a Near‐Surface Temperature Front in the Arctic Ocean

et al. 2020

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“…The AUV was deployed to autonomously collect high-resolution data across the front using adaptive sampling; see, for example, Fossum et al (2018). Both front detection and sampling location were decided by a state-based autonomous agent running onboard the AUV, optimizing data collection across and along the front.…”

Section: Autonomous Underwater Vehiclementioning

confidence: 99%

Observations of turbulence at a near-surface temperature front in the Arctic Ocean

Koenig

Fer

Kolås

et al. 2020

Preprint

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Measurements made at an Arctic thermohaline front show turbulence production through convection and forced symmetric instability, a mechanism drawing energy from the frontal geostrophic current. Destabilizing surface buoyancy fluxes from a combination of heat loss to the atmosphere and cross-front Ekman transport by down-front winds reduce the potential vorticity in the upper ocean. The front, located in the Nansen Basin close to the sea ice edge, separates the cold and fresh surface melt water from the warm and saline mixed layer. High resolution temperature, salinity, current and turbulence data were collected in the upper 100 m, on 18 September 2018 across the front from a research vessel and an autonomous underwater vehicle. The AUV was deployed to autonomously collect high resolution data across the front using adaptive sampling. Both front detection and sampling location were decided by a state-based autonomous agent running onboard the AUV, optimizing data collection across and along the front.In addition to convection by heat loss to atmosphere and mechanical forcing by moderate wind in the mixed layer, forced symmetric instability contributed with comparable magnitude in generation of turbulence at the front location down to 40 m depth. This turbulence was associated with turbulent heat fluxes of up to 10 W.m-2, eroding the warm and cold intrusions observed at respectively 35 and 55 m depth. A similar frontal structure has been crossed by a Seaglider in the same region 10 days after our survey. The submesoscale-to-turbulence scale transitions and resulting mixing can be widespread and important in the Atlantic sector of the Arctic Ocean.

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“…Several mathematical approaches from stochastic optimization viewpoints have been carried out to find cost‐efficient monitoring policies. Such applications include hydrodynamics, 20 animal movement, 21 population dynamics, 22 and soil environment 23 . Pagendam and Pollett 24 mathematically analyzed sampling intervals of birth‐death processes minimizing the expected confidence region of the parameter estimates.…”

Section: Introductionmentioning

confidence: 99%

Cost‐efficient monitoring of continuous‐time stochastic processes based on discrete observations

Yoshioka

Yaegashi

Tsujimura

et al. 2020

Appl Stoch Models Bus & Ind

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Planning a cost‐efficient monitoring policy of stochastic processes arises from many industrial problems. We formulate a simple discrete‐time monitoring problem of continuous‐time stochastic processes with its applications to several industrial problems. A key in our model is a doubling trick of the variables, with which we can construct an algorithm to solve the problem. The cost‐efficient monitoring policy balancing between the observation cost and information loss is governed by an optimality equation of a fixed point type, which is solvable with an iterative algorithm based on the Feynman‐Kac formula. This is a new linkage between monitoring problems and mathematical sciences. We show regularity results of the optimization problem and present a numerical algorithm for its approximation. A problem having model ambiguity is presented as well. The presented model is applied to problems of environment, ecology, and energy, having qualitatively different target stochastic processes with each other.

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Information‐driven robotic sampling in the coastal ocean

Cited by 51 publications

References 65 publications

Observations of Turbulence at a Near‐Surface Temperature Front in the Arctic Ocean

Observations of Turbulence at a Near‐Surface Temperature Front in the Arctic Ocean

Observations of turbulence at a near-surface temperature front in the Arctic Ocean

Cost‐efficient monitoring of continuous‐time stochastic processes based on discrete observations

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