Particles in the coastal ocean : theory and applications /

Summarizes the modeling of the transport, evolution and fate of particles in the coastal ocean for advanced students and researchers.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Lynch, Daniel R.
Otros Autores: Greenberg, D. A., Bilgili, Ata, McGillicuddy, Dennis J., Jr, Manning, James P., Aretxabaleta, Alfredo L., 1975-
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York : Cambridge University Press, 2014.
Temas:
Oceanography > Mathematical models.
Océanographie > Modèles mathématiques.
SCIENCE > Earth Sciences > Geography.
SCIENCE > Earth Sciences > Geology.
Oceanography > Mathematical models
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Half-title
  • Dedication
  • Title page
  • Copyright information
  • Table of contents
  • About the authors
  • Preface
  • Acknowledgments
  • List of acronyms
  • Definitions and notation
  • Introduction and scope
  • Part I Background
  • 1 The Coastal Ocean
  • 1.1 Typical Motions and Scales
  • 1.2 Particle Simulation
  • 1.2.1 Motion
  • 1.2.2 Rates
  • 1.2.3 Gather, Scatter
  • 1.2.4 Simulation
  • 1.2.5 Aggregation and Identity
  • 2 Drifters and Their Numerical Simulation
  • 2.1 Introduction
  • 2.2 Drifter Technology
  • 2.2.1 Design
  • 2.2.2 Communication
  • 2.2.3 Quality Control
  • 2.3 Particle Tracking
  • 2.3.1 Basic Lagrangian Model
  • 2.3.2 Practical Issues
  • 2.4 Model Validation with Drifters
  • 2.4.1 Field Experience
  • 2.5 Drifter Applications
  • 2.5.1 Drifter Assimilation
  • 3 Probability and Statistics
  • A Primer
  • 3.1 Basics
  • Random Numbers
  • 3.1.1 Continuous Distributions: f and F
  • 3.1.2 Properties: Survival, Hazard Rate
  • 3.1.3 Properties: Mean, Variance, Moments
  • 3.1.4 Properties: Median, Mode, Quartile
  • 3.1.5 Properties: Other Means
  • 3.1.6 Bounding Theorems
  • 3.1.7 Discrete Distributions: Pi and Fj
  • 3.2 Some Common Distributions
  • 3.2.1 Continuous Distributions
  • 3.2.2 Discrete Distributions
  • 3.2.3 Importance of G, U, B, Pois
  • 3.2.4 The Central Limit Theorem
  • 3.3 Generating Random Numbers
  • 3.3.1 General Methods
  • 3.3.2 Some Specific Deviates
  • 3.4 Sampling
  • Finite N
  • 3.4.1 Sample Statistics
  • 3.4.2 Sample Mean
  • 3.4.3 Sample Variance
  • 3.4.4 Recap
  • 3.5 Covariance
  • 3.5.1 Definitions
  • 3.5.2 Correlation and Autocorrelation
  • 3.5.3 Autocorrelated Time Series
  • 3.5.3.1 Separation-Based Covariance and Correlogram
  • 3.5.3.2 Correlogram and Impulse Response
  • 3.5.4 Autocorrelated Eulerian Fields
  • 3.5.5 Generating Covariance
  • 3.5.6 Summary
  • Covariance
  • 3.6 Particles in a Box
  • 3.6.1 Individual Residence Time
  • 3.6.2 Aggregate Properties: Relaxation of Initial Condition
  • 3.6.3 Export Rate
  • 3.6.4 Long-Run Balance
  • 3.6.5 Summary
  • Steady State
  • 3.6.6 Exit Paths
  • 3.6.7 Input Paths
  • 3.6.8 Autocorrelation
  • 3.6.9 Example
  • Branch Point
  • 3.6.10 A Network of Boxes
  • 3.6.10.1 Transfer Rate
  • 3.6.10.2 Steady State
  • 3.6.11 Closing Ideas
  • Particles in Boxes
  • 3.7 Closure
  • 3.8 General Sources
  • 4 Dispersion by Random Walk
  • 4.1 Introduction: Discrete Drunken Walk
  • 4.2 Continuous Processes
  • 4.2.1 Resolved and Subgrid Motion
  • 4.2.2 A Hierarchy
  • 4.3 The AR0 Model
  • Uncorrelated Random Walk and Simple Diffusion
  • 4.3.1 The Displacement Process
  • 4.3.2 Correspondence to Diffusion
  • 4.3.3 Multi-Dimensions
  • 4.3.4 Inhomogeneous Diffusion
  • 4.3.5 Anisotropic Diffusion
  • 4.3.6 Shear and Convergence
  • 4.3.7 Metrics of Resolution
  • 4.3.8 Stepsize and the Need for Autocorrelation
  • 4.4 The AR1 Model
  • Autocorrelated Velocity
  • 4.4.1 AR1: Continuous Form and Its Discretization

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