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Data Analysis Methods in Physical Oceanography [Kõva köide]

  • Formaat: Hardback, 400 pages, kõrgus: 250 mm, kaal: 1429 g, 8col.ill.figs.tabs.
  • Ilmumisaeg: 30-Jun-1997
  • Kirjastus: Pergamon
  • ISBN-10: 0080314341
  • ISBN-13: 9780080314341
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  • Formaat: Hardback, 400 pages, kõrgus: 250 mm, kaal: 1429 g, 8col.ill.figs.tabs.
  • Ilmumisaeg: 30-Jun-1997
  • Kirjastus: Pergamon
  • ISBN-10: 0080314341
  • ISBN-13: 9780080314341
Teised raamatud teemal:

Data Analysis Methods in Physical Oceanography, Third Edition is a practical reference to established and modern data analysis techniques in earth and ocean sciences. Its five major sections address data acquisition and recording, data processing and presentation, statistical methods and error handling, analysis of spatial data fields, and time series analysis methods. The revisedThird Edition updates the instrumentation used to collect and analyze physical oceanic data and adds new techniques including Kalman Filtering. Additionally, the sections covering spectral, wavelet, and harmonic analysis techniques are completely revised since these techniques have attracted significant attention over the past decade as more accurate and efficient data gathering and analysis methods.

  • Completely updated and revised to reflect new filtering techniques and major updating of the instrumentation used to collect and analyze data
  • Co-authored by scientists from academe and industry, both of whom have more than 30 years of experience in oceanographic research and field work
  • Significant revision of sections covering spectral, wavelet, and harmonic analysis techniques
  • Examples address typical data analysis problems yet provide the reader with formulaic “recipes for working with their own data
  • Significant expansion to 350 figures, illustrations, diagrams and photos
Preface xi(4)
Acknowledgments xv
Chapter 1 Data Acquisition and Recording
1(158)
1.1 Introduction
1(1)
1.2 Basic sampling requirements
2(6)
1.2.1 Sampling interval
3(1)
1.2.2 Sampling duration
4(2)
1.2.3 Sampling accuracy
6(1)
1.2.4 Burst sampling versus continuous sampling
6(1)
1.2.5 Regularly versus irregularly sampled data
7(1)
1.2.6 Independent realizations
8(1)
1.3 Temperature
8(25)
1.3.1 Mercury thermometers
9(3)
1.3.2 The mechanical bathythermograph (MBT)
12(2)
1.3.3 Resistance thermometers (expendable bathythermograph: XBT)
14(4)
1.3.4 Salinity/conductivity-temperature-depth profilers
18(1)
1.3.5 Dynamic response of temperature sensors
19(3)
1.3.6 Response times of CTD systems
22(1)
1.3.7 Temperature calibration of STD/CTD profilers
23(1)
1.3.8 Sea surface temperature
24(6)
1.3.9 The modern digital thermometer
30(2)
1.3.10 Potential temperature and density
32(1)
1.4 Salinity
33(9)
1.4.1 Salinity and electrical conductivity
34(5)
1.4.2 The practical salinity scale
39(3)
1.4.3 Nonconductive methods
42(1)
1.5 Depth or pressure
42(13)
1.5.1 Hydrostatic pressure
42(1)
1.5.2 Free-fall velocity
43(5)
1.5.3 Echo sounding
48(6)
1.5.4 Other depth sounding methods
54(1)
1.6 Sea-level measurement
55(13)
1.6.1 Tide and pressure gauges
58(4)
1.6.2 Satellite altimetry
62(1)
1.6.3 Inverted echo sounder (IES)
63(4)
1.6.4 Wave height and direction
67(1)
1.7 Eulerian currents
68(34)
1.7.1 Early current meter technology
70(1)
1.7.2 Rotor-type current meters
70(8)
1.7.3 Nonmechanical current meters
78(5)
1.7.4 Profiling acoustic Doppler current meters (ADCM)
83(11)
1.7.5 Comparisons of current meters
94(1)
1.7.6 Electromagnetic methods
95(1)
1.7.7 Other methods of current measurement
96(1)
1.7.8 Mooring logistics
97(2)
1.7.9 Acoustic releases
99(3)
1.8 Lagrangian current measurements
102(17)
1.8.1 Drift cards and bottles
103(1)
1.8.2 Modern drifters
104(3)
1.8.3 Processing satellite-tracked drifter data
107(2)
1.8.4 Drifter response
109(6)
1.8.5 Other types of surface drifters
115(1)
1.8.6 Subsurface floats
116(3)
1.8.7 Surface displacements in satellite imagery
119(1)
1.9 Wind
119(6)
1.10 Precipitation
125(2)
1.11 Chemical tracers
127(18)
1.11.1 Conventional tracers
128(10)
1.11.2 Light attenuation and scattering
138(4)
1.11.3 Oxygen isotope: XXX(18)O
142(1)
1.11.4 Helium-3; helium/heat ratio
143(2)
1.12 Transient chemical tracers
145(14)
1.12.1 Tritium
146(3)
1.12.2 Radiocarbon
149(4)
1.12.3 Chlorofluorocarbons
153(2)
1.12.4 Radon-222
155(2)
1.12.5 Sulfur hexachloride
157(1)
1.12.6 Strontium-90
158(1)
Chapter 2 Data Processing and Presentation
159(34)
2.1 Introduction
159(1)
2.2 Calibration
160(1)
2.3 Interpolation
161(1)
2.4 Data presentation
162(31)
2.4.1 Introduction
162(5)
2.4.2 Vertical profiles
167(3)
2.4.3 Vertical sections
170(2)
2.4.4 Horizontal maps
172(5)
2.4.5 Map projections
177(4)
2.4.6 Characteristic or property versus property diagrams
181(4)
2.4.7 Time-series presentation
185(2)
2.4.8 Histograms
187(1)
2.4.9 New directions in graphical presentation
187(6)
Chapter 3 Statistical Methods and Error Handling
193(112)
3.1 Introduction
193(1)
3.2 Sample distributions
194(3)
3.3 Probability
197(4)
3.3.1 Cumulative probability functions
200(1)
3.4 Moments and expected values
201(6)
3.4.1 Unbiased estimators and moments
203(1)
3.4.2 Moment generating functions
204(3)
3.5 Common probability density functions
207(4)
3.6 Central limit theorem
211(3)
3.7 Estimation
214(2)
3.8 Confidence intervals
216(8)
3.8.1 Confidence interval for XXX (XXX known)
217(1)
3.8.2 Confidence interval for XXX (XXX unknown)
218(1)
3.8.3 Confidence interval for XXX(2)
219(1)
3.8.4 Goodness-of-fit test
220(4)
3.9 Selecting the sample size
224(1)
3.10 Confidence intervals for altimeter bias estimates
225(2)
3.11 Estimation methods
227(6)
3.11.1 Minimum variance unbiased estimation
228(1)
3.11.2 Method of moments
229(1)
3.11.3 Maximum likelihood
230(3)
3.12 Linear estimation (regression)
233(10)
3.12.1 Method of least squares
234(4)
3.12.2 Standard error of the estimate
238(1)
3.12.3 Multivariate regression
239(1)
3.12.4 A computational example of matrix regression
240(2)
3.12.5 Polynomial curve fitting with least squares
242(1)
3.12.6 Relationship between least-squares and maximum likelihood
242(1)
3.13 Relationship between regression and correlation
243(6)
3.13.1 The effects of random errors on correlation
244(1)
3.13.2 The maximum likelihood correlation estimator
245(1)
3.13.3 Correlation and regression: cause and effect
246(3)
3.14 Hypothesis testing
249(8)
3.14.1 Significance levels and confidence intervals for correlation
253(1)
3.14.2 Analysis of variance and the F-distribution
254(3)
3.15 Effective degrees of freedom
257(9)
3.15.1 Trend estimates and the integral time scale
261(5)
3.16 Editing and despiking techniques: the nature of errors
266(11)
3.16.1 Identifying and removing errors
266(7)
3.16.2 Propagation of error
273(1)
3.16.3 Dealing with numbers: the statistics of roundoff
274(3)
3.16.4 Gauss-Markov theorem
277(1)
3.17 Interpolation: filling the data gaps
277(13)
3.17.1 Equally and unequally spaced data
277(2)
3.17.2 Interpolation methods
279(7)
3.17.3 Interpolating gappy records: practical examples
286(4)
3.18 Covariance and the covariance matrix
290(4)
3.18.1 Covariance and structure functions
291(1)
3.18.2 A computational example
291(2)
3.18.3 Multivariate distributions
293(1)
3.19 Bootstrap and jackknife methods
294(11)
3.19.1 Bootstrap method
295(6)
3.19.2 Jackknife method
301(4)
Chapter 4 The Spatial Analyses of Data Fields
305(66)
4.1 Traditional block and bulk averaging
305(4)
4.2 Objective analysis
309(10)
4.2.1 Objective mapping: examples
314(5)
4.3 Empirical orthogonal functions
319(25)
4.3.1 Principal axes of a single vector time series (scatter plot)
325(3)
4.3.2 EOF computation using the scatter matrix method
328(4)
4.3.3 EOF computation using singular value decomposition
332(2)
4.3.4 An example: deep currents near a mid-ocean ridge
334(2)
4.3.5 Interpretation of EOFs
336(4)
4.3.6 Variations on conventional EOF analysis
340(4)
4.4 Normal mode analysis
344(12)
4.4.1 Vertical normal modes
344(3)
4.4.2 An example: normal modes of semidiurnal frequency
347(3)
4.4.3 Coastal-trapped waves (CTWs)
350(6)
4.5 Inverse methods
356(15)
4.5.1 General inverse theory
356(5)
4.5.2 Inverse theory and absolute currents
361(5)
4.5.3 The IWEX internal wave problem
366(4)
4.4.4 Summary of inverse methods
370(1)
Chapter 5 Time-series Analysis Methods
371(198)
5.1 Basic concepts
371(2)
5.2 Stochastic processes and stationarity
373(1)
5.3 Correlation functions
374(6)
5.4 Fourier analysis
380(12)
5.4.1 Mathematical formulation
381(3)
5.4.2 Discrete time series
384(3)
5.4.3 A computational example
387(1)
5.4.4 Fourier analysis for specified frequencies
388(2)
5.4.5 The fast Fourier transform
390(2)
5.5 Harmonic analysis
392(12)
5.5.1 A least-squares method
392(3)
5.5.2 A computational example
395(2)
5.5.3 Harmonic analysis of tides
397(1)
5.5.4 Choice of constituents
398(1)
5.5.5 A computational example for tides
399(3)
5.5.6 Complex demodulation
402(2)
5.6 Spectral analysis
404(60)
5.6.1 Spectra of deterministic and stochastic processes
409(4)
5.6.2 Spectra of discrete series
413(4)
5.6.3 Conventional spectral methods
417(8)
5.6.4 Spectra of vector series
425(7)
5.6.5 Effect of sampling on spectral estimates
432(9)
5.6.6 Smoothing spectral estimates (windowing)
441(9)
5.6.7 Smoothing spectra in the frequency domain
450(4)
5.6.8 Confidence intervals on spectra
454(1)
5.6.9 Zero-padding and prewhitening
455(5)
5.6.10 Spectral analysis of unevenly spaced time series
460(1)
5.6.11 General spectral bandwidth and Q of the system
461(1)
5.6.12 Summary of the standard spectral analysis approach
461(3)
5.7 Spectral analysis (parametric methods)
464(16)
5.7.1 Some basic concepts
467(1)
5.7.2 Autoregressive power spectral estimation
468(10)
5.7.3 Maximum likelihood spectral estimation
478(2)
5.8 Cross-spectral analysis
480(21)
5.8.1 Cross-correlation functions
480(2)
5.8.2 Cross-covariance method
482(1)
5.8.3 Fourier transform method
482(2)
5.8.4 Phase and cross-amplitude functions
484(1)
5.8.5 Coincident and quadrature spectra
485(1)
5.8.6 Coherence spectrum (coherency)
486(4)
5.8.7 Frequency response of a linear system
490(5)
5.8.8 Rotary cross-spectral analysis
495(6)
5.9 Wavelet analysis
501(15)
5.9.1 The wavelet transform
502(2)
5.9.2 Wavelet algorithms
504(1)
5.9.3 Oceanographic examples
505(3)
5.9.4 The S-transformation
508(3)
5.9.5 The multiple filter technique
511(5)
5.10 Digital filters
516(41)
5.10.1 Introduction
518
5.10.2 Basic concepts
517(2)
5.10.3 Ideal filters
519(8)
5.10.4 Design of oceanographic filters
527(5)
5.10.5 Running-mean filters
532(3)
5.10.6 Godin-type filters
535(1)
5.10.7 Lanczos-window cosine filters
536(7)
5.10.8 Butterworth filters
543(8)
5.10.9 Frequency-domain (transform) filtering
551(6)
5.11 Fractals
557(12)
5.11.1 The scaling exponent method
561(1)
5.11.2 The yardstick method
562(1)
5.11.3 Box counting method
563(1)
5.11.4 Correlation dimension
564(1)
5.11.5 Dimensions of multifractal functions
564(3)
5.11.6 Predictability
567(2)
Appendices 569(24)
Appendix A Units in physical oceanography 570(2)
Appendix B Glossary of statistical terminology 572(4)
Appendix C Means, variances and moment-generating functions for some common continuous variables 576(1)
Appendix D Statistical tables 577(8)
Appendix E Correlation coefficents at the 5% and 1% levels of significance for various degrees of freedom v 585(1)
Appendix F Approximations and nondimensional numbers in physical oceanography 586(7)
References 593(24)
Index 617