Modeling methods for marine science:
This advanced textbook on modeling, data analysis and numerical techniques for marine science has been developed from a course taught by the authors for many years at the Woods Hole Oceanographic Institute. The first part covers statistics: singular value decomposition, error propagation, least squa...
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cambridge
Cambridge University Press
2011
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| Subjects: | |
| Links: | https://doi.org/10.1017/CBO9780511975721 https://doi.org/10.1017/CBO9780511975721 https://doi.org/10.1017/CBO9780511975721 |
| Summary: | This advanced textbook on modeling, data analysis and numerical techniques for marine science has been developed from a course taught by the authors for many years at the Woods Hole Oceanographic Institute. The first part covers statistics: singular value decomposition, error propagation, least squares regression, principal component analysis, time series analysis and objective interpolation. The second part deals with modeling techniques: finite differences, stability analysis and optimization. The third part describes case studies of actual ocean models of ever increasing dimensionality and complexity, starting with zero-dimensional models and finishing with three-dimensional general circulation models. Throughout the book hands-on computational examples are introduced using the MATLAB programming language and the principles of scientific visualization are emphasised. Ideal as a textbook for advanced students of oceanography on courses in data analysis and numerical modeling, the book is also an invaluable resource for a broad range of scientists undertaking modeling in chemical, biological, geological and physical oceanography |
| Item Description: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Physical Description: | 1 online resource (xv, 571 pages) |
| ISBN: | 9780511975721 |
| DOI: | 10.1017/CBO9780511975721 |
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| 100 | 1 | |a Glover, David M. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Modeling methods for marine science |c David M. Glover, William J. Jenkins, and Scott C. Doney |
| 264 | 1 | |a Cambridge |b Cambridge University Press |c 2011 | |
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| 500 | |a Title from publisher's bibliographic system (viewed on 05 Oct 2015) | ||
| 505 | 8 | |a 1. Resources, MATLAB primer, and introduction to linear algebra -- 1.1. Resources -- 1.2. Nomenclature -- 1.3. MATLAB primer -- 1.4. Basic linear algebra -- 2. Measurement theory, probability distributions, error propagation and analysis -- 2.1. Measurement theory -- 2.2. normal distribution -- 2.3. Doing the unspeakable: throwing out data points? -- 2.4. Error propagation -- 2.5. Statistical tests and the hypothesis -- 2.6. Other distributions -- 2.7. central limit theorem -- 2.8. Covariance and correlation -- 2.9. Basic non-parametric tests -- 2.10. Problems -- 3. Least squares and regression techniques, goodness of fit and tests, and nonlinear least squares techniques -- 3.1. Statistical basis for regression -- 3.2. Least squares fitting a straight line -- 3.3. General linear least squares technique -- 3.4. Nonlinear least squares techniques -- 4. Principal component and factor analysis -- 4.1. Conceptual foundations -- 4.2. Splitting and lumping -- | |
| 505 | 8 | |a 4.3. Optimum multiparameter (OMP) analysis -- 4.4. Principal component analysis (PCA) -- 4.5. Factor analysis -- 4.6. Empirical orthogonal functions (EOFs) -- 5. Sequence analysis I: Uniform series, cross- and autocorrelation, and Fourier transforms -- 5.1. Goals and examples of sequence analysis -- 5.2. ground rules: stationary processes, etc. -- 5.3. Analysis in time and space -- 5.4. Cross-covariance and cross-correlation -- 5.5. Convolution and implications for signal theory -- 5.6. Fourier synthesis and the Fourier transform -- 6. Sequence analysis II: Optimal filtering and spectral analysis -- 6.1. Optimal (and other) filtering -- 6.2. fast Fourier transform (FFT) -- 6.3. Power spectral analysis -- 6.4. Nyquist limits and data windowing -- 6.5. Non-uniform time series -- 6.6. Wavelet analysis -- 7. Gridding, objective mapping, and kriging -- 7.1. Contouring and gridding concepts -- 7.2. Structure functions -- 7.3. Optimal estimation -- 7.4. Kriging examples with real data -- | |
| 505 | 8 | |a 8. Integration of ODEs and 0D (box) models -- 8.1. ODE categorization -- 8.2. Examples of population or box models (0D) -- 8.3. Analytical solutions -- 8.4. Numerical integration techniques -- 8.5. numerical example -- 9. model building tutorial -- 9.1. Motivation and philosophy -- 9.2. Scales -- 9.3. First Example: The Lotka-Volterra model -- 9.4. second example: exploring our two-box phosphate model -- 9.5. third example: multi-box nutrient model of the world ocean | |
| 505 | 8 | |a 15. Upper ocean 1D seasonal models -- 15.1. Scope, background, and purpose -- 15.2. physical model framework -- 15.3. Atmospheric forcing -- 15.4. The physical model's internal workings -- 15.5. Implementing the physical model -- 15.6. Adding gases to the model -- 15.7. Implementing the gas model -- 15.8. Biological oxygen production in the model -- 16. Two-dimensional gyre models -- 16.1. Onward to the next dimension -- 16.2. two-dimensional advection-diffusion equation -- 16.3. Gridding and numerical considerations -- 16.4. Numerical diagnostics -- 16.5. Transient tracer invasion into a gyre -- 16.6. Doubling up for a better gyre model -- 16.7. Estimating oxygen utilization rates -- 16.8. Non-uniform grids -- 17. Three-dimensional general circulation models (GCMs) -- 17.1. Dynamics, governing equations, and approximations -- 17.2. Model grids and numerics -- 17.3. Surface boundary conditions -- 17.4. Sub-grid-scale parameterizations -- 17.5. Diagnostics and analyzing GCM output -- 18. Inverse methods and assimilation techniques -- 18.1. Generalized inverse theory -- 18.2. Solving under-determined systems -- 18.3. Ocean hydrographic inversions -- 18.4. Data assimilation methods -- 19. Scientific visualization -- 19.1. Why scientific visualization? -- 19.2. Data storage, manipulation, and access -- 19.3. perception of scientific data -- 19.4. Using MATLAB to present scientific data -- 19.5. Some non-MATLAB visualization tools -- 19.6. Advice on presentation graphics -- Getting started with MATLAB -- Good working practices -- Doing it faster -- Choose your algorithms wisely -- Automating tasks -- Graphical tricks -- Plotting oceanographic sections -- Reading and writing data | |
| 505 | 8 | |a 10. Model analysis and optimization -- 10.1. Basic concepts -- 10.2. Methods using only the cost function -- 10.3. Methods adding the cost function gradient -- 10.4. Stochastic algorithms -- 10.5. ecosystem optimization example -- 11. Advection-diffusion equations and turbulence -- 11.1. Rationale -- 11.2. basic equation -- 11.3. Reynolds decomposition -- 11.4. Stirring, straining, and mixing -- 11.5. importance of being non -- 11.6. numbers game -- 11.7. Vertical turbulent diffusion -- 11.8. Horizontal turbulent diffusion -- 11.9. effects of varying turbulent diffusivity -- 11.10. Isopycnal coordinate systems -- 12. Finite difference techniques -- 12.1. Basic principles -- 12.2. forward time, centered space (FTCS) algorithm -- 12.3. example: tritium and 3He in a pipe -- 12.4. Stability analysis of finite difference schemes -- 12.5. Upwind differencing schemes -- 12.6. Additional concerns, and generalities -- 12.7. Extension to more than one dimension -- 12.8. Implicit algorithms -- 13. Open ocean 1D advection-diffusion models -- 13.1. Rationale -- 13.2. general setting and equations -- 13.3. Stable conservative tracers: solving for K/w -- 13.4. Stable non-conservative tracers: solving for J/w -- 13.5. Radioactive non-conservative tracers: solving for w -- 13.6. Denouement: computing the other numbers -- 14. One-dimensional models in sedimentary systems -- 14.1. General theory -- 14.2. Physical and biological diagenetic processes -- 14.3. Chemical diagenetic processes -- 14.4. modeling example: CH4 at the FOAM site | |
| 520 | |a This advanced textbook on modeling, data analysis and numerical techniques for marine science has been developed from a course taught by the authors for many years at the Woods Hole Oceanographic Institute. The first part covers statistics: singular value decomposition, error propagation, least squares regression, principal component analysis, time series analysis and objective interpolation. The second part deals with modeling techniques: finite differences, stability analysis and optimization. The third part describes case studies of actual ocean models of ever increasing dimensionality and complexity, starting with zero-dimensional models and finishing with three-dimensional general circulation models. Throughout the book hands-on computational examples are introduced using the MATLAB programming language and the principles of scientific visualization are emphasised. Ideal as a textbook for advanced students of oceanography on courses in data analysis and numerical modeling, the book is also an invaluable resource for a broad range of scientists undertaking modeling in chemical, biological, geological and physical oceanography | ||
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Marine sciences / Mathematical models | |
| 700 | 1 | |a Jenkins, William J. |e Sonstige |4 oth | |
| 700 | 1 | |a Doney, Scott Christopher |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druckausgabe |z 978-0-521-86783-2 |
| 856 | 4 | 0 | |u https://doi.org/10.1017/CBO9780511975721 |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
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Record in the Search Index
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| author | Glover, David M. |
| author_facet | Glover, David M. |
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| bvnumber | BV043942844 |
| collection | ZDB-20-CBO |
| contents | 1. Resources, MATLAB primer, and introduction to linear algebra -- 1.1. Resources -- 1.2. Nomenclature -- 1.3. MATLAB primer -- 1.4. Basic linear algebra -- 2. Measurement theory, probability distributions, error propagation and analysis -- 2.1. Measurement theory -- 2.2. normal distribution -- 2.3. Doing the unspeakable: throwing out data points? -- 2.4. Error propagation -- 2.5. Statistical tests and the hypothesis -- 2.6. Other distributions -- 2.7. central limit theorem -- 2.8. Covariance and correlation -- 2.9. Basic non-parametric tests -- 2.10. Problems -- 3. Least squares and regression techniques, goodness of fit and tests, and nonlinear least squares techniques -- 3.1. Statistical basis for regression -- 3.2. Least squares fitting a straight line -- 3.3. General linear least squares technique -- 3.4. Nonlinear least squares techniques -- 4. Principal component and factor analysis -- 4.1. Conceptual foundations -- 4.2. Splitting and lumping -- 4.3. Optimum multiparameter (OMP) analysis -- 4.4. Principal component analysis (PCA) -- 4.5. Factor analysis -- 4.6. Empirical orthogonal functions (EOFs) -- 5. Sequence analysis I: Uniform series, cross- and autocorrelation, and Fourier transforms -- 5.1. Goals and examples of sequence analysis -- 5.2. ground rules: stationary processes, etc. -- 5.3. Analysis in time and space -- 5.4. Cross-covariance and cross-correlation -- 5.5. Convolution and implications for signal theory -- 5.6. Fourier synthesis and the Fourier transform -- 6. Sequence analysis II: Optimal filtering and spectral analysis -- 6.1. Optimal (and other) filtering -- 6.2. fast Fourier transform (FFT) -- 6.3. Power spectral analysis -- 6.4. Nyquist limits and data windowing -- 6.5. Non-uniform time series -- 6.6. Wavelet analysis -- 7. Gridding, objective mapping, and kriging -- 7.1. Contouring and gridding concepts -- 7.2. Structure functions -- 7.3. Optimal estimation -- 7.4. Kriging examples with real data -- 8. Integration of ODEs and 0D (box) models -- 8.1. ODE categorization -- 8.2. Examples of population or box models (0D) -- 8.3. Analytical solutions -- 8.4. Numerical integration techniques -- 8.5. numerical example -- 9. model building tutorial -- 9.1. Motivation and philosophy -- 9.2. Scales -- 9.3. First Example: The Lotka-Volterra model -- 9.4. second example: exploring our two-box phosphate model -- 9.5. third example: multi-box nutrient model of the world ocean 15. Upper ocean 1D seasonal models -- 15.1. Scope, background, and purpose -- 15.2. physical model framework -- 15.3. Atmospheric forcing -- 15.4. The physical model's internal workings -- 15.5. Implementing the physical model -- 15.6. Adding gases to the model -- 15.7. Implementing the gas model -- 15.8. Biological oxygen production in the model -- 16. Two-dimensional gyre models -- 16.1. Onward to the next dimension -- 16.2. two-dimensional advection-diffusion equation -- 16.3. Gridding and numerical considerations -- 16.4. Numerical diagnostics -- 16.5. Transient tracer invasion into a gyre -- 16.6. Doubling up for a better gyre model -- 16.7. Estimating oxygen utilization rates -- 16.8. Non-uniform grids -- 17. Three-dimensional general circulation models (GCMs) -- 17.1. Dynamics, governing equations, and approximations -- 17.2. Model grids and numerics -- 17.3. Surface boundary conditions -- 17.4. Sub-grid-scale parameterizations -- 17.5. Diagnostics and analyzing GCM output -- 18. Inverse methods and assimilation techniques -- 18.1. Generalized inverse theory -- 18.2. Solving under-determined systems -- 18.3. Ocean hydrographic inversions -- 18.4. Data assimilation methods -- 19. Scientific visualization -- 19.1. Why scientific visualization? -- 19.2. Data storage, manipulation, and access -- 19.3. perception of scientific data -- 19.4. Using MATLAB to present scientific data -- 19.5. Some non-MATLAB visualization tools -- 19.6. Advice on presentation graphics -- Getting started with MATLAB -- Good working practices -- Doing it faster -- Choose your algorithms wisely -- Automating tasks -- Graphical tricks -- Plotting oceanographic sections -- Reading and writing data 10. Model analysis and optimization -- 10.1. Basic concepts -- 10.2. Methods using only the cost function -- 10.3. Methods adding the cost function gradient -- 10.4. Stochastic algorithms -- 10.5. ecosystem optimization example -- 11. Advection-diffusion equations and turbulence -- 11.1. Rationale -- 11.2. basic equation -- 11.3. Reynolds decomposition -- 11.4. Stirring, straining, and mixing -- 11.5. importance of being non -- 11.6. numbers game -- 11.7. Vertical turbulent diffusion -- 11.8. Horizontal turbulent diffusion -- 11.9. effects of varying turbulent diffusivity -- 11.10. Isopycnal coordinate systems -- 12. Finite difference techniques -- 12.1. Basic principles -- 12.2. forward time, centered space (FTCS) algorithm -- 12.3. example: tritium and 3He in a pipe -- 12.4. Stability analysis of finite difference schemes -- 12.5. Upwind differencing schemes -- 12.6. Additional concerns, and generalities -- 12.7. Extension to more than one dimension -- 12.8. Implicit algorithms -- 13. Open ocean 1D advection-diffusion models -- 13.1. Rationale -- 13.2. general setting and equations -- 13.3. Stable conservative tracers: solving for K/w -- 13.4. Stable non-conservative tracers: solving for J/w -- 13.5. Radioactive non-conservative tracers: solving for w -- 13.6. Denouement: computing the other numbers -- 14. One-dimensional models in sedimentary systems -- 14.1. General theory -- 14.2. Physical and biological diagenetic processes -- 14.3. Chemical diagenetic processes -- 14.4. modeling example: CH4 at the FOAM site |
| ctrlnum | (ZDB-20-CBO)CR9780511975721 (OCoLC)900619441 (DE-599)BVBBV043942844 |
| dewey-full | 551.46015118 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 551 - Geology, hydrology, meteorology |
| dewey-raw | 551.46015118 |
| dewey-search | 551.46015118 |
| dewey-sort | 3551.46015118 |
| dewey-tens | 550 - Earth sciences |
| discipline | Geologie / Paläontologie |
| doi_str_mv | 10.1017/CBO9780511975721 |
| format | Electronic eBook |
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Contouring and gridding concepts -- 7.2. Structure functions -- 7.3. Optimal estimation -- 7.4. Kriging examples with real data -- </subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">8. Integration of ODEs and 0D (box) models -- 8.1. ODE categorization -- 8.2. Examples of population or box models (0D) -- 8.3. Analytical solutions -- 8.4. Numerical integration techniques -- 8.5. numerical example -- 9. model building tutorial -- 9.1. Motivation and philosophy -- 9.2. Scales -- 9.3. First Example: The Lotka-Volterra model -- 9.4. second example: exploring our two-box phosphate model -- 9.5. third example: multi-box nutrient model of the world ocean</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">15. Upper ocean 1D seasonal models -- 15.1. Scope, background, and purpose -- 15.2. physical model framework -- 15.3. Atmospheric forcing -- 15.4. The physical model's internal workings -- 15.5. Implementing the physical model -- 15.6. Adding gases to the model -- 15.7. Implementing the gas model -- 15.8. Biological oxygen production in the model -- 16. Two-dimensional gyre models -- 16.1. Onward to the next dimension -- 16.2. two-dimensional advection-diffusion equation -- 16.3. Gridding and numerical considerations -- 16.4. Numerical diagnostics -- 16.5. Transient tracer invasion into a gyre -- 16.6. Doubling up for a better gyre model -- 16.7. Estimating oxygen utilization rates -- 16.8. Non-uniform grids -- 17. Three-dimensional general circulation models (GCMs) -- 17.1. Dynamics, governing equations, and approximations -- 17.2. Model grids and numerics -- 17.3. Surface boundary conditions -- 17.4. Sub-grid-scale parameterizations -- 17.5. Diagnostics and analyzing GCM output -- 18. Inverse methods and assimilation techniques -- 18.1. Generalized inverse theory -- 18.2. Solving under-determined systems -- 18.3. Ocean hydrographic inversions -- 18.4. Data assimilation methods -- 19. Scientific visualization -- 19.1. Why scientific visualization? -- 19.2. Data storage, manipulation, and access -- 19.3. perception of scientific data -- 19.4. Using MATLAB to present scientific data -- 19.5. Some non-MATLAB visualization tools -- 19.6. Advice on presentation graphics -- Getting started with MATLAB -- Good working practices -- Doing it faster -- Choose your algorithms wisely -- Automating tasks -- Graphical tricks -- Plotting oceanographic sections -- Reading and writing data</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">10. Model analysis and optimization -- 10.1. Basic concepts -- 10.2. Methods using only the cost function -- 10.3. Methods adding the cost function gradient -- 10.4. Stochastic algorithms -- 10.5. ecosystem optimization example -- 11. Advection-diffusion equations and turbulence -- 11.1. Rationale -- 11.2. basic equation -- 11.3. Reynolds decomposition -- 11.4. Stirring, straining, and mixing -- 11.5. importance of being non -- 11.6. numbers game -- 11.7. Vertical turbulent diffusion -- 11.8. Horizontal turbulent diffusion -- 11.9. effects of varying turbulent diffusivity -- 11.10. Isopycnal coordinate systems -- 12. Finite difference techniques -- 12.1. Basic principles -- 12.2. forward time, centered space (FTCS) algorithm -- 12.3. example: tritium and 3He in a pipe -- 12.4. Stability analysis of finite difference schemes -- 12.5. Upwind differencing schemes -- 12.6. Additional concerns, and generalities -- 12.7. Extension to more than one dimension -- 12.8. Implicit algorithms -- 13. Open ocean 1D advection-diffusion models -- 13.1. Rationale -- 13.2. general setting and equations -- 13.3. Stable conservative tracers: solving for K/w -- 13.4. Stable non-conservative tracers: solving for J/w -- 13.5. Radioactive non-conservative tracers: solving for w -- 13.6. Denouement: computing the other numbers -- 14. One-dimensional models in sedimentary systems -- 14.1. General theory -- 14.2. Physical and biological diagenetic processes -- 14.3. Chemical diagenetic processes -- 14.4. modeling example: CH4 at the FOAM site</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This advanced textbook on modeling, data analysis and numerical techniques for marine science has been developed from a course taught by the authors for many years at the Woods Hole Oceanographic Institute. The first part covers statistics: singular value decomposition, error propagation, least squares regression, principal component analysis, time series analysis and objective interpolation. The second part deals with modeling techniques: finite differences, stability analysis and optimization. The third part describes case studies of actual ocean models of ever increasing dimensionality and complexity, starting with zero-dimensional models and finishing with three-dimensional general circulation models. Throughout the book hands-on computational examples are introduced using the MATLAB programming language and the principles of scientific visualization are emphasised. 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| id | DE-604.BV043942844 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T17:49:19Z |
| institution | BVB |
| isbn | 9780511975721 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029351814 |
| oclc_num | 900619441 |
| open_access_boolean | |
| owner | DE-12 DE-92 |
| owner_facet | DE-12 DE-92 |
| physical | 1 online resource (xv, 571 pages) |
| psigel | ZDB-20-CBO ZDB-20-CBO BSB_PDA_CBO ZDB-20-CBO FHN_PDA_CBO |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Cambridge University Press |
| record_format | marc |
| spelling | Glover, David M. Verfasser aut Modeling methods for marine science David M. Glover, William J. Jenkins, and Scott C. Doney Cambridge Cambridge University Press 2011 1 online resource (xv, 571 pages) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 05 Oct 2015) 1. Resources, MATLAB primer, and introduction to linear algebra -- 1.1. Resources -- 1.2. Nomenclature -- 1.3. MATLAB primer -- 1.4. Basic linear algebra -- 2. Measurement theory, probability distributions, error propagation and analysis -- 2.1. Measurement theory -- 2.2. normal distribution -- 2.3. Doing the unspeakable: throwing out data points? -- 2.4. Error propagation -- 2.5. Statistical tests and the hypothesis -- 2.6. Other distributions -- 2.7. central limit theorem -- 2.8. Covariance and correlation -- 2.9. Basic non-parametric tests -- 2.10. Problems -- 3. Least squares and regression techniques, goodness of fit and tests, and nonlinear least squares techniques -- 3.1. Statistical basis for regression -- 3.2. Least squares fitting a straight line -- 3.3. General linear least squares technique -- 3.4. Nonlinear least squares techniques -- 4. Principal component and factor analysis -- 4.1. Conceptual foundations -- 4.2. Splitting and lumping -- 4.3. Optimum multiparameter (OMP) analysis -- 4.4. Principal component analysis (PCA) -- 4.5. Factor analysis -- 4.6. Empirical orthogonal functions (EOFs) -- 5. Sequence analysis I: Uniform series, cross- and autocorrelation, and Fourier transforms -- 5.1. Goals and examples of sequence analysis -- 5.2. ground rules: stationary processes, etc. -- 5.3. Analysis in time and space -- 5.4. Cross-covariance and cross-correlation -- 5.5. Convolution and implications for signal theory -- 5.6. Fourier synthesis and the Fourier transform -- 6. Sequence analysis II: Optimal filtering and spectral analysis -- 6.1. Optimal (and other) filtering -- 6.2. fast Fourier transform (FFT) -- 6.3. Power spectral analysis -- 6.4. Nyquist limits and data windowing -- 6.5. Non-uniform time series -- 6.6. Wavelet analysis -- 7. Gridding, objective mapping, and kriging -- 7.1. Contouring and gridding concepts -- 7.2. Structure functions -- 7.3. Optimal estimation -- 7.4. Kriging examples with real data -- 8. Integration of ODEs and 0D (box) models -- 8.1. ODE categorization -- 8.2. Examples of population or box models (0D) -- 8.3. Analytical solutions -- 8.4. Numerical integration techniques -- 8.5. numerical example -- 9. model building tutorial -- 9.1. Motivation and philosophy -- 9.2. Scales -- 9.3. First Example: The Lotka-Volterra model -- 9.4. second example: exploring our two-box phosphate model -- 9.5. third example: multi-box nutrient model of the world ocean 15. Upper ocean 1D seasonal models -- 15.1. Scope, background, and purpose -- 15.2. physical model framework -- 15.3. Atmospheric forcing -- 15.4. The physical model's internal workings -- 15.5. Implementing the physical model -- 15.6. Adding gases to the model -- 15.7. Implementing the gas model -- 15.8. Biological oxygen production in the model -- 16. Two-dimensional gyre models -- 16.1. Onward to the next dimension -- 16.2. two-dimensional advection-diffusion equation -- 16.3. Gridding and numerical considerations -- 16.4. Numerical diagnostics -- 16.5. Transient tracer invasion into a gyre -- 16.6. Doubling up for a better gyre model -- 16.7. Estimating oxygen utilization rates -- 16.8. Non-uniform grids -- 17. Three-dimensional general circulation models (GCMs) -- 17.1. Dynamics, governing equations, and approximations -- 17.2. Model grids and numerics -- 17.3. Surface boundary conditions -- 17.4. Sub-grid-scale parameterizations -- 17.5. Diagnostics and analyzing GCM output -- 18. Inverse methods and assimilation techniques -- 18.1. Generalized inverse theory -- 18.2. Solving under-determined systems -- 18.3. Ocean hydrographic inversions -- 18.4. Data assimilation methods -- 19. Scientific visualization -- 19.1. Why scientific visualization? -- 19.2. Data storage, manipulation, and access -- 19.3. perception of scientific data -- 19.4. Using MATLAB to present scientific data -- 19.5. Some non-MATLAB visualization tools -- 19.6. Advice on presentation graphics -- Getting started with MATLAB -- Good working practices -- Doing it faster -- Choose your algorithms wisely -- Automating tasks -- Graphical tricks -- Plotting oceanographic sections -- Reading and writing data 10. Model analysis and optimization -- 10.1. Basic concepts -- 10.2. Methods using only the cost function -- 10.3. Methods adding the cost function gradient -- 10.4. Stochastic algorithms -- 10.5. ecosystem optimization example -- 11. Advection-diffusion equations and turbulence -- 11.1. Rationale -- 11.2. basic equation -- 11.3. Reynolds decomposition -- 11.4. Stirring, straining, and mixing -- 11.5. importance of being non -- 11.6. numbers game -- 11.7. Vertical turbulent diffusion -- 11.8. Horizontal turbulent diffusion -- 11.9. effects of varying turbulent diffusivity -- 11.10. Isopycnal coordinate systems -- 12. Finite difference techniques -- 12.1. Basic principles -- 12.2. forward time, centered space (FTCS) algorithm -- 12.3. example: tritium and 3He in a pipe -- 12.4. Stability analysis of finite difference schemes -- 12.5. Upwind differencing schemes -- 12.6. Additional concerns, and generalities -- 12.7. Extension to more than one dimension -- 12.8. Implicit algorithms -- 13. Open ocean 1D advection-diffusion models -- 13.1. Rationale -- 13.2. general setting and equations -- 13.3. Stable conservative tracers: solving for K/w -- 13.4. Stable non-conservative tracers: solving for J/w -- 13.5. Radioactive non-conservative tracers: solving for w -- 13.6. Denouement: computing the other numbers -- 14. One-dimensional models in sedimentary systems -- 14.1. General theory -- 14.2. Physical and biological diagenetic processes -- 14.3. Chemical diagenetic processes -- 14.4. modeling example: CH4 at the FOAM site This advanced textbook on modeling, data analysis and numerical techniques for marine science has been developed from a course taught by the authors for many years at the Woods Hole Oceanographic Institute. The first part covers statistics: singular value decomposition, error propagation, least squares regression, principal component analysis, time series analysis and objective interpolation. The second part deals with modeling techniques: finite differences, stability analysis and optimization. The third part describes case studies of actual ocean models of ever increasing dimensionality and complexity, starting with zero-dimensional models and finishing with three-dimensional general circulation models. Throughout the book hands-on computational examples are introduced using the MATLAB programming language and the principles of scientific visualization are emphasised. Ideal as a textbook for advanced students of oceanography on courses in data analysis and numerical modeling, the book is also an invaluable resource for a broad range of scientists undertaking modeling in chemical, biological, geological and physical oceanography Mathematisches Modell Marine sciences / Mathematical models Jenkins, William J. Sonstige oth Doney, Scott Christopher Sonstige oth Erscheint auch als Druckausgabe 978-0-521-86783-2 https://doi.org/10.1017/CBO9780511975721 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Glover, David M. Modeling methods for marine science 1. Resources, MATLAB primer, and introduction to linear algebra -- 1.1. Resources -- 1.2. Nomenclature -- 1.3. MATLAB primer -- 1.4. Basic linear algebra -- 2. Measurement theory, probability distributions, error propagation and analysis -- 2.1. Measurement theory -- 2.2. normal distribution -- 2.3. Doing the unspeakable: throwing out data points? -- 2.4. Error propagation -- 2.5. Statistical tests and the hypothesis -- 2.6. Other distributions -- 2.7. central limit theorem -- 2.8. Covariance and correlation -- 2.9. Basic non-parametric tests -- 2.10. Problems -- 3. Least squares and regression techniques, goodness of fit and tests, and nonlinear least squares techniques -- 3.1. Statistical basis for regression -- 3.2. Least squares fitting a straight line -- 3.3. General linear least squares technique -- 3.4. Nonlinear least squares techniques -- 4. Principal component and factor analysis -- 4.1. Conceptual foundations -- 4.2. Splitting and lumping -- 4.3. Optimum multiparameter (OMP) analysis -- 4.4. Principal component analysis (PCA) -- 4.5. Factor analysis -- 4.6. Empirical orthogonal functions (EOFs) -- 5. Sequence analysis I: Uniform series, cross- and autocorrelation, and Fourier transforms -- 5.1. Goals and examples of sequence analysis -- 5.2. ground rules: stationary processes, etc. -- 5.3. Analysis in time and space -- 5.4. Cross-covariance and cross-correlation -- 5.5. Convolution and implications for signal theory -- 5.6. Fourier synthesis and the Fourier transform -- 6. Sequence analysis II: Optimal filtering and spectral analysis -- 6.1. Optimal (and other) filtering -- 6.2. fast Fourier transform (FFT) -- 6.3. Power spectral analysis -- 6.4. Nyquist limits and data windowing -- 6.5. Non-uniform time series -- 6.6. Wavelet analysis -- 7. Gridding, objective mapping, and kriging -- 7.1. Contouring and gridding concepts -- 7.2. Structure functions -- 7.3. Optimal estimation -- 7.4. Kriging examples with real data -- 8. Integration of ODEs and 0D (box) models -- 8.1. ODE categorization -- 8.2. Examples of population or box models (0D) -- 8.3. Analytical solutions -- 8.4. Numerical integration techniques -- 8.5. numerical example -- 9. model building tutorial -- 9.1. Motivation and philosophy -- 9.2. Scales -- 9.3. First Example: The Lotka-Volterra model -- 9.4. second example: exploring our two-box phosphate model -- 9.5. third example: multi-box nutrient model of the world ocean 15. Upper ocean 1D seasonal models -- 15.1. Scope, background, and purpose -- 15.2. physical model framework -- 15.3. Atmospheric forcing -- 15.4. The physical model's internal workings -- 15.5. Implementing the physical model -- 15.6. Adding gases to the model -- 15.7. Implementing the gas model -- 15.8. Biological oxygen production in the model -- 16. Two-dimensional gyre models -- 16.1. Onward to the next dimension -- 16.2. two-dimensional advection-diffusion equation -- 16.3. Gridding and numerical considerations -- 16.4. Numerical diagnostics -- 16.5. Transient tracer invasion into a gyre -- 16.6. Doubling up for a better gyre model -- 16.7. Estimating oxygen utilization rates -- 16.8. Non-uniform grids -- 17. Three-dimensional general circulation models (GCMs) -- 17.1. Dynamics, governing equations, and approximations -- 17.2. Model grids and numerics -- 17.3. Surface boundary conditions -- 17.4. Sub-grid-scale parameterizations -- 17.5. Diagnostics and analyzing GCM output -- 18. Inverse methods and assimilation techniques -- 18.1. Generalized inverse theory -- 18.2. Solving under-determined systems -- 18.3. Ocean hydrographic inversions -- 18.4. Data assimilation methods -- 19. Scientific visualization -- 19.1. Why scientific visualization? -- 19.2. Data storage, manipulation, and access -- 19.3. perception of scientific data -- 19.4. Using MATLAB to present scientific data -- 19.5. Some non-MATLAB visualization tools -- 19.6. Advice on presentation graphics -- Getting started with MATLAB -- Good working practices -- Doing it faster -- Choose your algorithms wisely -- Automating tasks -- Graphical tricks -- Plotting oceanographic sections -- Reading and writing data 10. Model analysis and optimization -- 10.1. Basic concepts -- 10.2. Methods using only the cost function -- 10.3. Methods adding the cost function gradient -- 10.4. Stochastic algorithms -- 10.5. ecosystem optimization example -- 11. Advection-diffusion equations and turbulence -- 11.1. Rationale -- 11.2. basic equation -- 11.3. Reynolds decomposition -- 11.4. Stirring, straining, and mixing -- 11.5. importance of being non -- 11.6. numbers game -- 11.7. Vertical turbulent diffusion -- 11.8. Horizontal turbulent diffusion -- 11.9. effects of varying turbulent diffusivity -- 11.10. Isopycnal coordinate systems -- 12. Finite difference techniques -- 12.1. Basic principles -- 12.2. forward time, centered space (FTCS) algorithm -- 12.3. example: tritium and 3He in a pipe -- 12.4. Stability analysis of finite difference schemes -- 12.5. Upwind differencing schemes -- 12.6. Additional concerns, and generalities -- 12.7. Extension to more than one dimension -- 12.8. Implicit algorithms -- 13. Open ocean 1D advection-diffusion models -- 13.1. Rationale -- 13.2. general setting and equations -- 13.3. Stable conservative tracers: solving for K/w -- 13.4. Stable non-conservative tracers: solving for J/w -- 13.5. Radioactive non-conservative tracers: solving for w -- 13.6. Denouement: computing the other numbers -- 14. One-dimensional models in sedimentary systems -- 14.1. General theory -- 14.2. Physical and biological diagenetic processes -- 14.3. Chemical diagenetic processes -- 14.4. modeling example: CH4 at the FOAM site Mathematisches Modell Marine sciences / Mathematical models |
| title | Modeling methods for marine science |
| title_auth | Modeling methods for marine science |
| title_exact_search | Modeling methods for marine science |
| title_full | Modeling methods for marine science David M. Glover, William J. Jenkins, and Scott C. Doney |
| title_fullStr | Modeling methods for marine science David M. Glover, William J. Jenkins, and Scott C. Doney |
| title_full_unstemmed | Modeling methods for marine science David M. Glover, William J. Jenkins, and Scott C. Doney |
| title_short | Modeling methods for marine science |
| title_sort | modeling methods for marine science |
| topic | Mathematisches Modell Marine sciences / Mathematical models |
| topic_facet | Mathematisches Modell Marine sciences / Mathematical models |
| url | https://doi.org/10.1017/CBO9780511975721 |
| work_keys_str_mv | AT gloverdavidm modelingmethodsformarinescience AT jenkinswilliamj modelingmethodsformarinescience AT doneyscottchristopher modelingmethodsformarinescience |