Lagrangian Stochastic Modeling in Coastal Oceanography

Brickman, David; Smith, Peter C.

doi:10.1175/1520-0426(2002)019<0083:lsmico>2.0.co;2

Cited by 81 publications

(76 citation statements)

References 31 publications

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“…Brickman & Smith (2002) identify 2 concerns relating to particle tracking, which they term underseeding problems. Both concerns relate to the number of particles that must be tracked.…”

Section: Discussionmentioning

confidence: 99%

“…The second concern relates to the probability of detecting rare events in a stochastic process. Brickman & Smith (2002) discuss both problems in terms of the relative size of the spawning and nursery areas. If spawning and nursery areas are coincident and large, then only a small number of particles and a few simulations may be needed, because we are trying to estimate the average of a process.…”

Section: Discussionmentioning

confidence: 99%

“…As the field has developed, the particletracking algorithms have become more sophisticated, with increasing attention being paid to the statistical aspects of subgrid-scale motion (Heath et al 1998). Even when the model incorporates the correct statistical distribution of subgrid-scale motion, proper interpretation of the distribution of trajectories of individual larvae remains challenging (Brickman & Smith 2002).…”

Section: Coupling Hydrodynamics and Biologymentioning

confidence: 99%

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Contribution of individual-based coupled physicalbiological models to understanding recruitment in marine fish populations

Miller¹

2007

Mar. Ecol. Prog. Ser.

162

109

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Annual publications involving the application of coupled physical-biological models for understanding fish recruitment processes have increased over the last decade. Sixty-nine papers were reviewed to assess the contribution these models have made to recruitment prediction and understanding. The majority of models reviewed were 2-and 3-dimensional numerical simulation models, although a limited number of 1-dimensional analytical models were included. Most models used a Lagrangian tracking algorithm to advect and diffuse particles within the model domain. The vertical and horizontal resolutions and temporal durations of the models varied widely. This review identified 3 categories of papers: explanatory, inferential and hypothesis generating. Reviewed papers were dominated by explanatory approaches. Assessment of the sensitivity of model predictions to the model parameters were rare, but not entirely absent in this group of papers. Inferential approaches were the next most common, and sought to infer the presence or role of a particular mechanism. Hypothesis-generating publications were the rarest, but perhaps have the most to contribute to a greater understanding of recruitment processes. An increase in the frequency of hypothesis-generating applications of coupled physical-biological models may be expected over time as the field matures and refinements to both the physical and biological processes included in the models are made.

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“…Brickman & Smith (2002) identify 2 concerns relating to particle tracking, which they term underseeding problems. Both concerns relate to the number of particles that must be tracked.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Coupling Hydrodynamics and Biologymentioning

confidence: 99%

See 1 more Smart Citation

Contribution of individual-based coupled physicalbiological models to understanding recruitment in marine fish populations

Miller¹

2007

Mar. Ecol. Prog. Ser.

162

109

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“…[7] The statistical certainty of probability distributions computed with Monte Carlo techniques strongly depends on the number of numerical integrations considered in the ensemble [Brickman and Smith, 2002]. Increasing complexity and variability of the drifting field implies a higher number of numerical integrations in the ensemble to get confident results.…”

Section: Introductionmentioning

confidence: 99%

A Fourier‐transform path integral formalism to compute dispersion probability distributions in variable ocean environments

Álvarez

Pennel

Garau

et al. 2007

Geophysical Research Letters

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[1] Computing the drift of objects and substances by ocean currents is of high relevance in operational oceanography and environmental managing. This problem is usually solved by a probability distribution computed from an ensemble of simulations of a stochastic differential equation. The accuracy of the results depends on the number of simulations considered in the ensemble. An approach based on a path integral formalism is proposed to compute distribution probabilities. The path integral is solved by a novel algorithm involving Fourier transforms. The best performance of this path integral approach versus traditional matrix multiplication and Monte Carlo techniques is demonstrated by comparing results of probability distributions of dispersion in ocean environments with different spatial scales of variability. Results indicate that the new algorithm provides a probability distribution of dispersion with similar accuracy as that obtained by the Monte Carlo approach, but a hundred times faster. Citation: Alvarez, A., R. Pennel, B. Garau, and J. Tintore (2007), A Fourier-transform path integral formalism to compute dispersion probability distributions in variable ocean environments, Geophys. Res. Lett., 34, L17605,

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“…Además, hubiera sido útil contar con una descripción de la topografía del dominio de mayor resolución para evaluar que tan apropiado es extrapolar los datos de la estación EMAC a toda el área de simulación. Respecto al número de partículas liberadas en cada condición, hubiera sido deseable una mejor justificación de un número que parece excesivamente bajo, incluso para una descripción cualitativa de la dispersión (ver por ejemplo Brickman & Smith 2002). En suma, la definición de los experimentos numéricos en base a cuyos resultados se pretende poner a prueba la hipótesis de Alvarez et al (2011), resulta incompleta y en varios aspectos dificulta la comprensión de sus resultados.…”

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Comentarios a 'Aplicación de un modelo de trazadores lagrangianos en Bahía Anegada, Argentina' de Alvarez et al. (2011)

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Olivares

2013

Rev. biol. mar. oceanogr.

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Lagrangian Stochastic Modeling in Coastal Oceanography

Cited by 81 publications

References 31 publications

Contribution of individual-based coupled physicalbiological models to understanding recruitment in marine fish populations

Contribution of individual-based coupled physicalbiological models to understanding recruitment in marine fish populations

A Fourier‐transform path integral formalism to compute dispersion probability distributions in variable ocean environments

Comentarios a 'Aplicación de un modelo de trazadores lagrangianos en Bahía Anegada, Argentina' de Alvarez et al. (2011)

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Lagrangian Stochastic Modeling in Coastal Oceanography

Cited by 81 publications

References 31 publications

Contribution of individual-based coupled physicalbiological models to understanding recruitment in marine fish populations

Contribution of individual-based coupled physicalbiological models to understanding recruitment in marine fish populations

A Fourier‐transform path integral formalism to compute dispersion probability distributions in variable ocean environments

Comentarios a 'Aplicación de un modelo de trazadores lagrangianos en Bahía Anegada, Argentina' de Alvarez et al. (2011)

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Contribution of individual-based coupled physicalbiological models to understanding recruitment in marine fish populations

Contribution of individual-based coupled physicalbiological models to understanding recruitment in marine fish populations