We surveyed Antarctic krill (Euphausia superba) under sea ice using the autonomous underwater vehicle Autosub-2. Krill were concentrated within a band under ice between 1 and 13 kilometers south of the ice edge. Within this band, krill densities were fivefold greater than that of open water. The under-ice environment has long been considered an important habitat for krill, but sampling difficulties have previously prevented direct observations under ice over the scale necessary for robust krill density estimation. Autosub-2 enabled us to make continuous high-resolution measurements of krill density under ice reaching 27 kilometers beyond the ice edge.
The cavities beneath Antarctic ice shelves are among the least studied regions of the World Ocean, yet they are sites of globally important water mass transformations. Here we report results from a mission beneath Fimbul Ice Shelf of an autonomous underwater vehicle. The data reveal a spatially complex oceanographic environment, an ice base with widely varying roughness, and a cavity periodically exposed to water with a temperature significantly above the surface freezing point. The results of this, the briefest of glimpses of conditions in this extraordinary environment, are already reforming our view of the topographic and oceanographic conditions beneath ice shelves, holding out great promises for future missions from similar platforms.
The underwater glider is set to become an important platform for oceanographers to gather data within oceans. Gliders are usually equipped with a conductivity-temperature-depth (CTD) sensor, but a wide range of other sensors have been fitted to gliders.In the present work, the authors aim at measuring the vertical water velocity. The vertical water velocity is obtained by subtracting the vertical glider velocity relative to the water from the vertical glider velocity relative to the water surface. The latter is obtained from the pressure sensor. For the former, a quasi-static model of planar glider flight is developed. The model requires three calibration parameters, the (parasite) drag coefficient, glider volume (at atmospheric pressure), and hull compressibility, which are found by minimizing a cost function based on the variance of the calculated vertical water velocity.Vertical water velocities have been calculated from data gathered in the northwestern Mediterranean during the Gulf of Lions experiment, winter 2008. Although no direct comparison could be made with water velocities from an independent measurement technique, the authors show that, for two different heat loss regimes ('0 and '400 W m 22 ), the calculated vertical velocity scales are comparable with those expected for internal waves and active open ocean convection, respectively. High noise levels resulting from the pressure sensor require the water velocity time series to be low-pass filtered with a cutoff period of 80 s. The absolute accuracy of the vertical water velocity is estimated at 64 mm s 21.
The aim of this community white paper is to make recommendations for a glider component of a global ocean observing system. We first recommend the adoption of an Argo-like data system for gliders. Then, we argue that combining glider deployments with the other components (ships, moorings, floats and satellites) will considerably enhance our capacity for observing the ocean by filling gaps left by the other observing systems. Gliders could be deployed to sample most of the western and eastern boundary circulations and the regional seas (around 20 basins in the world) which are not well covered by the present global ocean observing system and in the vicinity of fixed point time series stations. These plans already involve people scattered around the world in Australia, Canada, Cyprus, France, Germany, Italy, Norway, Spain, UK, and the USA, and will certainly expand to many other countries. A rough estimate of resources required is about 13M$/Euro for ~20+ gliders permanently at sea during five years in the world ocean, based on present scientific infrastructures.
Autonomous Underwater Vehicles (AUVs) are effective platforms for science research and 9 monitoring, and for military and commercial data-gathering purposes. However, there is an inevitable risk of 10 loss during any mission. Quantifying the risk of loss is complex, due to the combination of vehicle reliability 11 and environmental factors, and cannot be determined through analytical means alone. An alternative 12 approach -formal expert judgment -is a time-consuming process; consequently a method is needed to 13 broaden the applicability of judgments beyond the narrow confines of an elicitation for a defined 14 environment. We propose and explore a solution founded on a Bayesian Belief Network (BBN), where the 15 results of the expert judgment elicitation are taken as the initial prior probability of loss due to failure. The 16 network topology captures the causal effects of the environment separately on the vehicle and on the 17 support platform, and combines these to produce an updated probability of loss due to failure. An extended 18 version of the Kaplan Meier estimator is then used to update the mission risk profile with travelled distance. 19Sensitivity analysis of the BBN is presented and a case study of Autosub3 AUV deployment in the Amundsen 20Sea is discussed in detail. 22Keywords: Bayesian networks, survival statistics, expert judgment elicitation, autonomous vehicles. Autonomous Underwater Vehicles (AUVs) have a future as effective platforms for science research and 2 monitoring, and for military and commercial data-gathering purposes. Increasingly they are being used in 3 environments that are not benign [1,2]. Environments such as under sea ice [3], under shelf ice [4], or along 4 rocky coasts [5] intuitively give rise to a higher risk of loss should the vehicle malfunction. The risk of loss is 5 real; for example, Australian and British AUVs have been lost under ice sheets [6] and one team maintained a 6 lightweight tether to an AUV when operating under sea ice. The problem of predicting risk of loss is not only 7 one of predicting the reliability of the vehicle as a whole, its sub-systems and its components, but also of how 8 the operating environment, together with reliability, sets the probability of losing the vehicle. It is not obvious 9 that an approach based on separate statistical analyses of vehicle reliability and the affects of the 10 environment on probability of loss is either feasible or meaningful. Such an approach, when reduced to 11 summary statistics such as mean time to failure, would ignore the interaction between individual faults or 12 incidents and the environment, which we postulate to be at the centre of this problem. 13One alternative would be to assess the probability of loss in various environments directly, by counting the 14 frequency of occurrence. This frequentist approach is certainly appropriate for assessing the reliability of 15 identical engineered systems, where probability of failure is derived from a long-run frequency of occurrence, 16usually fr...
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