Evaluation of the search and rescue LEEWAY model in the Tyrrhenian Sea: a new point of view

Maio, Antonia Di; Martin, M. V.; Sorgente, Roberto

doi:10.5194/nhess-16-1979-2016

Cited by 19 publications

(10 citation statements)

References 38 publications

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“…These results show that TRAPs may be completely hidden in instantaneous streamline and horizontal divergence plots, yet predict the shortterm fate of passive tracers, as well as inertial objects influenced by windage, such as drifters. Although incorporating inertial, windage, and leeway effects could, in principle, provide a better prediction, in a SAR operation the inertia of the target objects is generally unknown 14 and wind information is unavailable.…”

Section: Resultsmentioning

confidence: 99%

Search and rescue at sea aided by hidden flow structures

et al. 2020

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Every year, hundreds of people die at sea because of vessel and airplane accidents. A key challenge in reducing the number of these fatalities is to make Search and Rescue (SAR) algorithms more efficient. Here, we address this challenge by uncovering hidden TRansient Attracting Profiles (TRAPs) in ocean-surface velocity data. Computable from a single velocity-field snapshot, TRAPs act as short-term attractors for all floating objects. In three different ocean field experiments, we show that TRAPs computed from measured as well as modeled velocities attract deployed drifters and manikins emulating people fallen in the water. TRAPs, which remain hidden to prior flow diagnostics, thus provide critical information for hazard responses, such as SAR and oil spill containment, and hence have the potential to save lives and limit environmental disasters.

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Section: Resultsmentioning

confidence: 99%

Search and rescue at sea aided by hidden flow structures

et al. 2020

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“…Finally, the search areas may be more complex than just an ellipse; it may well be a set of disjoint areas, each one with an associated different POC (e.g. Abi-Zeid and Frost, 2005;Breivik and Allen, 2008;Guitouni and Masri, 2014;Di Maio et al, 2016). Figure 17 shows the evolution of the ellipse areas, averaged over all IABP buoys.…”

Section: Predictive Skills Of Nextsim and Of The Fd Modelsmentioning

confidence: 99%

Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic

Rabatel¹,

Rampal²,

Carrassi³

et al. 2018

The Cryosphere

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Abstract. We present a sensitivity analysis and discuss the probabilistic forecast capabilities of the novel sea ice model neXtSIM used in hindcast mode. The study pertains to the response of the model to the uncertainty on winds using probabilistic forecasts of ice trajectories. neXtSIM is a continuous Lagrangian numerical model that uses an elasto-brittle rheology to simulate the ice response to external forces. The sensitivity analysis is based on a Monte Carlo sampling of 12 members. The response of the model to the uncertainties is evaluated in terms of simulated ice drift distances from their initial positions, and from the mean position of the ensemble, over the mid-term forecast horizon of 10 days. The simulated ice drift is decomposed into advective and diffusive parts that are characterised separately both spatially and temporally and compared to what is obtained with a free-drift model, that is, when the ice rheology does not play any role in the modelled physics of the ice. The seasonal variability of the model sensitivity is presented and shows the role of the ice compactness and rheology in the ice drift response at both local and regional scales in the Arctic. Indeed, the ice drift simulated by neXtSIM in summer is close to the one obtained with the free-drift model, while the more compact and solid ice pack shows a significantly different mechanical and drift behaviour in winter. For the winter period analysed in this study, we also show that, in contrast to the free-drift model, neXtSIM reproduces the sea ice Lagrangian diffusion regimes as found from observed trajectories. The forecast capability of neXtSIM is also evaluated using a large set of real buoy's trajectories and compared to the capability of the freedrift model. We found that neXtSIM performs significantly better in simulating sea ice drift, both in terms of forecast error and as a tool to assist search and rescue operations, although the sources of uncertainties assumed for the present experiment are not sufficient for complete coverage of the observed IABP positions.

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“…New advancements in leeway calculation have shown that it is possible to create a model of leeway drift using the balance of hydrodynamic and aerodynamic forces. This was conducted by Di Maio et al (2016) on a person in the water, with the modeled leeway performing better than the statistical approach described above. If this model proves accurate with other objects, it could reduce the need for direct measurement of leeway parameters.…”

Section: Search and Rescue Fundamentalsmentioning

confidence: 99%

Search and Rescue Applications: On the Need to Improve Ocean Observing Data Systems in Offshore or Remote Locations

Futch

Allen

2019

Front. Mar. Sci.

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Search and rescue (SAR) in remote maritime locations is a difficult mission. One of the limitations in these isolated regions is the low density of available oceanographic data for model validation. In order to examine the state of remote search and rescue a review of maritime search theory and advances was conducted. This included basic drift theory, leeway, available environmental data, and the current methods used by the United States Coast Guard for SAR operations. In particular the U.S. Coast Guard's fourteenth district's SAR case history was examined and it was found that 60% of SAR cases fall outside of areas that have high-resolution wind and current data, with only global scale model forecasts available. In addition, 2% of cases occurred in offshore waters (> 12 nm from land) and exceeded 36 h in asset response time. Three SAR simulations were run off the coast of Oahu, Hawaii using the same wind data but different surface current models. These simulations had extremely large (up to 12,000 km 2) search areas, highlighting the need for solutions that narrow these expected areas.

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Evaluation of the search and rescue LEEWAY model in the Tyrrhenian Sea: a new point of view

Cited by 19 publications

References 38 publications

Search and rescue at sea aided by hidden flow structures

Search and rescue at sea aided by hidden flow structures

Impact of rheology on probabilistic forecasts of sea ice trajectories: application for search and rescue operations in the Arctic

Search and Rescue Applications: On the Need to Improve Ocean Observing Data Systems in Offshore or Remote Locations

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