Modeling methods for marine science /:
This is a textbook on modelling, data analysis and numerical techniques for advanced students and researchers in chemical, biological, geological and physical oceanography.
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge ; New York, N.Y. :
Cambridge University Press,
2011.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This is a textbook on modelling, data analysis and numerical techniques for advanced students and researchers in chemical, biological, geological and physical oceanography. |
| Beschreibung: | 1 online resource (xv, 571 pages :) |
| Bibliographie: | Includes bibliographical references (pages 552-563) and index. |
| ISBN: | 9781139144728 1139144723 9780511975721 0511975724 9781139137386 1139137387 1316088081 9781316088081 1139139738 9781139139731 1283315068 9781283315067 1139138944 9781139138949 9786613315069 6613315060 1139140515 9781139140515 1139141406 9781139141406 |
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| 100 | 1 | |a Glover, David M. |0 http://id.loc.gov/authorities/names/n82054262 | |
| 245 | 1 | 0 | |a Modeling methods for marine science / |c David M. Glover, William J. Jenkins, and Scott C. Doney. |
| 260 | |a Cambridge ; |a New York, N.Y. : |b Cambridge University Press, |c 2011. | ||
| 300 | |a 1 online resource (xv, 571 pages :) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 504 | |a Includes bibliographical references (pages 552-563) and index. | ||
| 520 | 8 | |a This is a textbook on modelling, data analysis and numerical techniques for advanced students and researchers in chemical, biological, geological and physical oceanography. | |
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |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 -- 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. A 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 | 0 | |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. | |
| 505 | 0 | |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 -- Appendix A. Hints and tricks. 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. | |
| 546 | |a English. | ||
| 650 | 0 | |a Marine sciences |x Mathematical models. | |
| 650 | 0 | |a Oceanography |x Mathematical models. | |
| 650 | 0 | |a Marine sciences |v Statistics. | |
| 650 | 0 | |a Oceanography |v Statistics. | |
| 650 | 6 | |a Sciences de la mer |x Modèles mathématiques. | |
| 650 | 6 | |a Océanographie |x Modèles mathématiques. | |
| 650 | 6 | |a Sciences de la mer |v Statistiques. | |
| 650 | 6 | |a Océanographie |v Statistiques. | |
| 650 | 7 | |a NATURE |x Ecosystems & Habitats |x Oceans & Seas. |2 bisacsh | |
| 650 | 7 | |a SCIENCE |x Earth Sciences |x Oceanography. |2 bisacsh | |
| 650 | 0 | 7 | |a Ocanografía |x Modelos matemáticos |2 embucm |
| 650 | 7 | |a Marine sciences |2 fast | |
| 650 | 7 | |a Oceanography |2 fast | |
| 650 | 7 | |a Oceanography |x Mathematical models |2 fast | |
| 655 | 7 | |a Statistics |2 fast | |
| 700 | 1 | |a Jenkins, William J. |0 http://id.loc.gov/authorities/names/nb2011016345 | |
| 700 | 1 | |a Doney, Scott Christopher. |0 http://id.loc.gov/authorities/names/no99091940 | |
| 758 | |i has work: |a Modeling methods for marine science (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFv3BR3j888C6cMwGjpGxP |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Glover, David M., Dr. |t Modeling methods for marine science. |d Cambridge ; New York : Cambridge University Press, 2011 |z 9780521867832 |w (DLC) 2011284602 |w (OCoLC)690090203 |
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Datensatz im Suchindex
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|---|---|
| _version_ | 1813903418093207552 |
| adam_text | |
| any_adam_object | |
| author | Glover, David M. |
| author2 | Jenkins, William J. Doney, Scott Christopher |
| author2_role | |
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| author_GND | http://id.loc.gov/authorities/names/n82054262 http://id.loc.gov/authorities/names/nb2011016345 http://id.loc.gov/authorities/names/no99091940 |
| author_facet | Glover, David M. Jenkins, William J. Doney, Scott Christopher |
| author_role | |
| author_sort | Glover, David M. |
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| bvnumber | localFWS |
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| callnumber-label | GC10 |
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| callnumber-search | GC10.4.M36 G56 2011eb |
| callnumber-sort | GC 210.4 M36 G56 42011EB |
| callnumber-subject | GC - Oceanography |
| collection | ZDB-4-EBA |
| 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. A 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. 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. 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 -- Appendix A. Hints and tricks. 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. |
| ctrlnum | (OCoLC)760411194 |
| dewey-full | 551.4/6/015118 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 551 - Geology, hydrology, meteorology |
| dewey-raw | 551.4/6/015118 |
| dewey-search | 551.4/6/015118 |
| dewey-sort | 3551.4 16 515118 |
| dewey-tens | 550 - Earth sciences |
| discipline | Geologie / Paläontologie |
| format | Electronic eBook |
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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. A 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="0" 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="505" ind1="0" 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. 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| genre_facet | Statistics |
| id | ZDB-4-EBA-ocn760411194 |
| illustrated | Illustrated |
| indexdate | 2024-10-25T16:18:22Z |
| institution | BVB |
| isbn | 9781139144728 1139144723 9780511975721 0511975724 9781139137386 1139137387 1316088081 9781316088081 1139139738 9781139139731 1283315068 9781283315067 1139138944 9781139138949 9786613315069 6613315060 1139140515 9781139140515 1139141406 9781139141406 |
| language | English |
| oclc_num | 760411194 |
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| owner | MAIN |
| owner_facet | MAIN |
| physical | 1 online resource (xv, 571 pages :) |
| psigel | ZDB-4-EBA |
| publishDate | 2011 |
| publishDateSearch | 2011 |
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| publisher | Cambridge University Press, |
| record_format | marc |
| spelling | Glover, David M. http://id.loc.gov/authorities/names/n82054262 Modeling methods for marine science / David M. Glover, William J. Jenkins, and Scott C. Doney. Cambridge ; New York, N.Y. : Cambridge University Press, 2011. 1 online resource (xv, 571 pages :) text txt rdacontent still image sti rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references (pages 552-563) and index. This is a textbook on modelling, data analysis and numerical techniques for advanced students and researchers in chemical, biological, geological and physical oceanography. Print version record. 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. A 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. 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. 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 -- Appendix A. Hints and tricks. 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. English. Marine sciences Mathematical models. Oceanography Mathematical models. Marine sciences Statistics. Oceanography Statistics. Sciences de la mer Modèles mathématiques. Océanographie Modèles mathématiques. Sciences de la mer Statistiques. Océanographie Statistiques. NATURE Ecosystems & Habitats Oceans & Seas. bisacsh SCIENCE Earth Sciences Oceanography. bisacsh Ocanografía Modelos matemáticos embucm Marine sciences fast Oceanography fast Oceanography Mathematical models fast Statistics fast Jenkins, William J. http://id.loc.gov/authorities/names/nb2011016345 Doney, Scott Christopher. http://id.loc.gov/authorities/names/no99091940 has work: Modeling methods for marine science (Text) https://id.oclc.org/worldcat/entity/E39PCFv3BR3j888C6cMwGjpGxP https://id.oclc.org/worldcat/ontology/hasWork Print version: Glover, David M., Dr. Modeling methods for marine science. Cambridge ; New York : Cambridge University Press, 2011 9780521867832 (DLC) 2011284602 (OCoLC)690090203 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=400633 Volltext CBO01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=400633 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. A 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. 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. 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 -- Appendix A. Hints and tricks. 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. Marine sciences Mathematical models. Oceanography Mathematical models. Marine sciences Statistics. Oceanography Statistics. Sciences de la mer Modèles mathématiques. Océanographie Modèles mathématiques. Sciences de la mer Statistiques. Océanographie Statistiques. NATURE Ecosystems & Habitats Oceans & Seas. bisacsh SCIENCE Earth Sciences Oceanography. bisacsh Ocanografía Modelos matemáticos embucm Marine sciences fast Oceanography fast Oceanography Mathematical models fast |
| 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 | Marine sciences Mathematical models. Oceanography Mathematical models. Marine sciences Statistics. Oceanography Statistics. Sciences de la mer Modèles mathématiques. Océanographie Modèles mathématiques. Sciences de la mer Statistiques. Océanographie Statistiques. NATURE Ecosystems & Habitats Oceans & Seas. bisacsh SCIENCE Earth Sciences Oceanography. bisacsh Ocanografía Modelos matemáticos embucm Marine sciences fast Oceanography fast Oceanography Mathematical models fast |
| topic_facet | Marine sciences Mathematical models. Oceanography Mathematical models. Marine sciences Statistics. Oceanography Statistics. Sciences de la mer Modèles mathématiques. Océanographie Modèles mathématiques. Sciences de la mer Statistiques. Océanographie Statistiques. NATURE Ecosystems & Habitats Oceans & Seas. SCIENCE Earth Sciences Oceanography. Ocanografía Modelos matemáticos Marine sciences Oceanography Oceanography Mathematical models Statistics |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=400633 |
| work_keys_str_mv | AT gloverdavidm modelingmethodsformarinescience AT jenkinswilliamj modelingmethodsformarinescience AT doneyscottchristopher modelingmethodsformarinescience |