High resolution modeling and data assimilation in the Monterey Bay area

Shulman, Igor; Wu, Chi‐Hua; Lewis, James K.; Paduan, Jeffrey D.; Rosenfeld, Leslie K.; Kindle, John C.; Ramp, Steven R.; Collins, Curtis A.

doi:10.1016/s0278-4343(01)00100-5

Cited by 46 publications

(41 citation statements)

References 15 publications

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“…To overcome this difference, turbulence closure is achieved by using a non-isotropic eddy viscosity coefficient. Here, the non-isotropic coefficient is obtained through the sum of an isotropic coefficient ν t and an horizontal eddy viscosity coefficient ν h,t , as customary in oceanographic applications (Roman et al, 2010;Shulman et al, 2002). The first term is given by the following equation:…”

Section: The 3d Hydrodynamic Model and The Numerical Simulationsmentioning

confidence: 99%

Three-dimensional numerical simulations on wind- and tide-induced currents: The case of Augusta Harbour (Italy)

Marchis

Freni

Napoli

2014

Computers & Geosciences

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a b s t r a c tThe hydrodynamic circulation in the coastal area of the Augusta Bay (Italy), located in the eastern part of Sicily, is analysed. Due to the heavy contamination generated by the several chemical and petrochemical industries active in the zone, the harbour was declared a Contaminated Site of National Interest. To mitigate the risks connected with the industrial activities located near the harbour, it is important to analyse the hydrodynamic circulation in the coastal area. To perform such analysis, a parallel 3D numerical model is used to solve the Reynolds-averaged momentum and mass balance, employing the k-ε turbulence model for the Reynolds stresses. The numerical model is parallelized using the programing technology -Message Passing Interface (MPI) and applying the domain decomposition procedure.The Augusta Bay circulation is mainly due to the relative contribution of the wind force acting over the free surface and the tidal motion through the mouths. Due to the geometric complexity of the domain and the presence of several piers along the coast, a curvilinear boundary-fitted computational grid was used, where cells corresponding to land areas or to wharfs were excluded from the computation. Comparisons between numerical results and field measurements were performed. Three different simulations were performed to selectively isolate the effect of each force, wind and tide, acting in the considered domain. The current in the basin was successfully estimated on the basis of the numerical results, demonstrating the specific role of wind and tidal oscillation in the hydrodynamic circulation inside the harbour.

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Section: The 3d Hydrodynamic Model and The Numerical Simulationsmentioning

confidence: 99%

Three-dimensional numerical simulations on wind- and tide-induced currents: The case of Augusta Harbour (Italy)

Marchis

Freni

Napoli

2014

Computers & Geosciences

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“…The ICON model has an orthogonal, curvilinear grid with horizontal resolution ranging from 1 to 4 km. The model has 30 vertical sigma levels (Shulman et al 2002). At the smaller scale, a fine-resolution submodel (frsICON) has been set up within the ICON model domain (Fig.…”

Section: Circulation Modelsmentioning

confidence: 99%

Bioluminescence Intensity Modeling and Sampling Strategy Optimization*

Shulman

Kindle

McGillicuddy

et al. 2005

Journal of Atmospheric and Oceanic Technology

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The focus of this paper is on the development of methodology for short-term (1-3 days) oceanic bioluminescence (BL) predictions and the optimization of spatial and temporal bioluminescence sampling strategies. The approach is based on predictions of bioluminescence with an advection-diffusion-reaction (tracer) model with velocities and diffusivities from a circulation model. In previous research, it was shown that short-term changes in some of the salient features in coastal bioluminescence can be explained and predicted by using this approach. At the same time, it was demonstrated that optimization of bioluminescence sampling prior to the forecast is critical for successful short-term BL predictions with the tracer model. In the present paper, the adjoint to the tracer model is used to study the sensitivity of the modeled bioluminescence distributions to the sampling strategies for BL. The locations and times of bioluminescence sampling prior to the forecast are determined by using the adjoint-based sensitivity maps. The approach is tested with bioluminescence observations collected during August 2000 and 2003 in the Monterey Bay, California, area. During August 2000, BL surveys were collected during a strong wind relaxation event, while in August 2003, BL surveys were conducted during an extended (longer than a week) upwellingfavorable event. The numerical bioluminescence predictability experiments demonstrated a close agreement between observed and model-predicted short-term spatial and temporal changes of the coastal bioluminescence.

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“…Data were available outside the control volume from several moorings, drifters deployed by the Monterey Bay Aquarium Research Institute (MBARI) and other ships and vehicles. Data were assimilated regularly into three different high-resolution ocean models: the Harvard Ocean Prediction System (HOPS) [95], the Jet Propulsion Laboratory implementation of the Regional Oceanic Modeling System (JPL/ROMS) [96] and the Navy Coastal Ocean Model/Innovative Coastal Ocean Observing Network (NCOM/ICON) [97], each of which produced daily updated ocean predictions of temperature, salinity and velocity. All observational data and model outputs were made available in near-real time on a central data server at MBARI.…”

Section: Cooperative Gliders In Asapmentioning

confidence: 99%

Cooperative Vehicle Environmental Monitoring

Leonard

2016

Springer Handbook of Ocean Engineering

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Over the last decade, methodologies for automated cooperative control of robotic vehicles have been designed, deployed and proven to provide efficient, reliable, and sustained monitoring of the uncertain and inhospitable ocean environment. Unprecedented data sets have been collected from deployments of cooperative vehicles in the field, and both real-time and post-deployment analyses have led to new understanding of the environment. This first decade of success in cooperative vehicle environmental monitoring sets the stage for new opportunities and future gain, especially as the development of cooperative control methodologies can continue to leverage ongoing technological and scientific advances in underwater communication and sensing, energy and computational efficiency, vehicle size, speed, maneuverability and cost, and ocean modeling and prediction.Indeed, the demonstrated potential of cooperative vehicle control has led to increased demand for fleets of autonomous underwater vehicles (AUVs) for use in measuring ocean physics, biology, chemistry and geology to improve understanding of natural dynamics and human-influenced changes in the marine environment. Further, methodologies for cooperative control of robotic vehicles in the ocean are readily adaptable to applications on land, in the air and in space; likewise, there is much to be learned from developments in these other domains. The recent explosion in research on networks and complex systems, including investigation of mechanisms that explain a "collective intelligence" exhibited by animal aggregations on the move, are also being leveraged to advance design of cooperative vehicle dynamics.For environmental monitoring to be successful, physical, chemical, and biological variables must be measured across a range of spatial and temporal scales; in the ocean the monitoring strategy must also contend with a harsh, three-dimensional physical space that is highly uncertain and dynamic. Small spatial and temporal scales associated with the measured variables typically make a stationary sensor array impractical because a very large number of sensors would be needed to get sufficient resolution in space and/or time. An array of mobile sensors, however, may be very well suited to such a challenge since mobility can be exploited to dynamically distribute fewer sensors according to the spatial and temporal scales.The underlying principle of cooperative control of vehicles for environmental monitoring leverages mobility of sensors and uses an interacting dynamic among the individual sensors to yield a collective behavior that performs better than the sum of the parts. If the vehicles can communicate their state or measure the relative state of others in the team, then they can cooperate and the cooperative vehicle dynamics can provide coordinated motion of the team as a whole. The resulting vehicle network functions as a dynamically reconfigurable sensor array with a capability for high performance in environmental monitoring not available at the level of individual...

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High resolution modeling and data assimilation in the Monterey Bay area

Cited by 46 publications

References 15 publications

Three-dimensional numerical simulations on wind- and tide-induced currents: The case of Augusta Harbour (Italy)

Three-dimensional numerical simulations on wind- and tide-induced currents: The case of Augusta Harbour (Italy)

Bioluminescence Intensity Modeling and Sampling Strategy Optimization*

Cooperative Vehicle Environmental Monitoring

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