Analysis of the Tampa Bay Coastal Prediction System

Vincent, Mark; Luther, Mark E.; Ross, Mark

doi:10.1061/40628(268)64

Cited by 5 publications

(6 citation statements)

References 4 publications

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“…The present version is a 3D, primitive equation, time-dependent model originally developed by Vincent (2001). The model uses a sigma coordinate system, a curvilinear grid that is fit to the shoreline, a split time step for the solution of the baroclinic 3D mode and the barotropic 2D mode, and an embedded Mellor-Yamada second-order turbulence closure model (Mellor and Yamada 1982) to provide vertical mixing coefficients.…”

Section: Methodsmentioning

confidence: 99%

A Numerical Simulation of Residual Circulation in Tampa Bay. Part II: Lagrangian Residence Time

Meyers

Luther

2008

Estuaries and Coasts

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Lagrangian retention and flushing are examined by advecting neutrally buoyant point particles within a circulation field generated by a numerical ocean model of Tampa Bay. Large temporal variations in Lagrangian residence time are found under realistic changes in boundary conditions. Two 90-day time periods are examined. The first (P1) is characterized by low freshwater inflow and weak baroclinic circulation. The second (P2) has high freshwater inflow and strong baroclinic circulation. At the beginning of both time periods, 686,400 particles are released uniformly throughout the bay. Issues relating to particle distribution and flushing are examined at three different spatial scales: (1) at the scale of the entire bay, (2) the four major regions within the bay, and (3) at the scale of individual model grid cells. Two simple theoretical models for the particle number over time, N(t), are fit to the particle counts from the ocean model. The theoretical models are shown to represent N(t) reasonably well when considering the entire bay, allowing for straightforward calculation of baywide residence times: 156 days for P1 and 36 days for P2. However, the accuracy of these simple models decreases with decreasing spatial scale. This is likely due to the fact that particles may exit, reenter, or redistribute from one region to another in any sequence. The smaller the domain under consideration, the more this exchange process dominates. Therefore, definitions of residence time need to be modified for "non-local" situations. After choosing a reasonable definition, and removal of the tidal and synoptic signals, the residence times at each grid cell in P1 is found to vary spatially from a few days to 90 days, the limit of the calculation, with an average residence time of 53 days. For P2, the overall spatial pattern is more homogeneous, and the residence times have an average value of 26 days.

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Section: Methodsmentioning

confidence: 99%

A Numerical Simulation of Residual Circulation in Tampa Bay. Part II: Lagrangian Residence Time

Meyers

Luther

2008

Estuaries and Coasts

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“…The model has been calibrated to the geometry and dynamics of the bay and shows reliable performance in near real time for water level, and current nowcasting. 15 The equations of motion solved in the model are for continuity, horizontal and vertical momentum, temperature transport, salinity transport, turbulent kinetic-energy transport, and turbulent macroscale. A particle advection and tracking subroutine was added to the model.…”

Section: Remote Intelligence (Numerical Model)mentioning

confidence: 99%

“…The model is a version of the Princeton Ocean Model, modified for Tampa Bay applications. 14 The model is a fully nonlinear finite-difference model that utilizes a surface-and bottom-following sigma coordinate system in the vertical and is set in an orthogonal curvilinear grid in the horizontal. The finite-difference equations conserve energy, mass, and momentum.…”

Section: Remote Intelligence (Numerical Model)mentioning

confidence: 99%

In‐water field analytical technology: Underwater mass spectrometry, mobile robots, and remote intelligence for wide and local area chemical profiling

Fries

Short

Langebrake

et al. 2001

Field Analytical Chem & Tech

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An automated field analysis strategy for aqueous environments is proposed with the use of a mobile robot equipped with an underwater mass spectrometer aided by linked remote numerical models or natural intelligence. Intelligent search strategies were made possible through the use of numerical models, and natural intelligence was in the form of a man in the loop. The field-analysis strategy is useful for local-and wide-area in situ chemical surveying for environmental and economical tasks. The operation of chemoreceptive field underwater robots is demonstrated during two field trials, one using numerical models to aid in characterization of chemical dispersion, and the other discriminating a chemical gradient in the field.

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“…A database system capable of data storage and management provides access to past and present data in processing predictions and allows for comparisons with observations. Numerous systems that integrate real-time observations and a three-dimensional (3D) hydrodynamic model, so-called nowcasting/forecasting systems, have been developed over the past 20 years in coastal oceanographic regions such as the Florida Straits (Mooers and Ko 1994), Tampa Bay (Hess et al 1996;Vincent et al 2001), the eastern U.S. coast (Aikman et al 1996), San Francisco Bay (Cheng and Casulli 1996), Chesapeake Bay (Bosley 1996), Galveston Bay (Schmalz 1998), and the New York Harbor (Wei and Sun 1998).…”

Section: Introductionmentioning

confidence: 99%

Real-time observation and prediction of physical processes in a typhoon-affected lake

Kimura

Liu

Chiu

et al. 2011

Paddy Water Environ

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A nowcasting system integrating observations and a numerical model was developed to better understand the spatial distributions and temporal variations in the dynamics of a small, subtropical lake occasionally influenced by severe rainstorms. The nowcasting system was used to monitor real-time meteorological conditions and thermal structures, to provide spatial information on the thermal and flow dynamics from model predictions, and to compare the predictions with the observations. The system was also able to determine whether the instruments collecting field data were performing correctly using telemetry operations. The nowcasting system was initially operated in the spring of 2009 in Yuan-Yang Lake, Taiwan. Rainstorm-induced mixing occurred due to Typhoon Morakot during August 7-9, 2009. The mixing was observed by the instruments, and the spatial distributions and temporal variations during the mixing were successfully predicted by the three-dimensional hydrodynamic lake model. A quantitative comparison of the energy balances among the heat, wind, and water inflow inputs to the lake implied that the typhoon-induced mixing was primarily caused by strong winds. The model predictions showed that the lake experienced mixing and flooding (large amounts of inflow/ outflow discharges), resulting in homogenous temperatures and flows that moved to the outlet of the lake.

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Analysis of the Tampa Bay Coastal Prediction System

Cited by 5 publications

References 4 publications

A Numerical Simulation of Residual Circulation in Tampa Bay. Part II: Lagrangian Residence Time

A Numerical Simulation of Residual Circulation in Tampa Bay. Part II: Lagrangian Residence Time

In‐water field analytical technology: Underwater mass spectrometry, mobile robots, and remote intelligence for wide and local area chemical profiling

Real-time observation and prediction of physical processes in a typhoon-affected lake

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