Call number:
AWI G2-98-0260
Description / Table of Contents:
Data Analysis Methods in Physical Oceanography provides a comprehensive
and practical compilation of the essential information and analysis techniques
required for the advanced processing and interpretation of digital spatiatemporal
data in physical oceanography as well in other branches of the
geophysical sciences.
This book assumes a fundamental understanding of calculus and is directed
primarily towards scientists and engineers in industry, government and
universities, including graduate and advanced undergraduate students.
Spanning five chapters and numerous appendices, the book provides a
valuable compendium of the fundamental data processing tools required by
the marine scientist.
Many of these tools will be of use in other branches of the physical and
natural sciences. The book begins with detailed discussion of the
instruments used to collect oceanographic data and the limitation of the
resulting data. Data presentation and display methods are reviewed in
chapter two. The remaining three chapters supply detailed information on a
broad range of statistical and deterministic data analysis methods ranging
from established methods such as Analysis of Variance methods and
Principal Component Analysis, to more recent data analysis techniques such
as Wavelet Transforms and Fractals. Each technique is illustrated by a
worked example and a large number of references are given for the reader
who may want to dig deeper into the subject. No other book of this type
exists that brings together in one volume information on the measurement
systems, data editing, data reduction/processing and analysis and
interpretational. This book brings all of this information into a single volume
which can act as a text for the neophyte or a reference volume for the
experienced scientist. The book is both a guide and an encyclopaedia to
modern data processing methods in the geophysical sciences. Many nonoceanographers
should find this volume a handy reference on their shelves.
Type of Medium:
Monograph available for loan
Pages:
XVI, 634 S. : Ill., graph. Darst., Kt.
Edition:
1st ed.
ISBN:
0080314341
Language:
English
Note:
Contents:
Preface. -
Acknowledgments. -
Chapter 1 Data Acquisition and Recording. -
1.1 Introduction. -
1.2 Basic sampling requirements. -
1.2.1 Sampling interval. -
1.2.2 Sampling duration. -
1.2.3 Sampling accuracy. -
1.2.4 Burst sampling versus continuous sampling. -
1.2.5 Regularly versus irregularly sampled data. -
1.2.6 Independent realizations. -
1.3 Temperature. -
1.3.1 Mercury thermometers. -
1.3.2 The mechanical bathythermograph (MBT). -
1.3.3 Resistance thermometers (expendable bathythermograph: XBT). -
1.3.4 Salinity/conductivity-temperature-depth profilers. -
1.3.5 Dynamic response of temperature sensors 19
1.3.6 Response times of CTD systems. -
1.3.7 Temperature calibration of STD/CTD profilers. -
1.3.8 Sea surface temperature. -
1.3.9 The modern digital thermometer. -
1.3.10 Potential temperature and density. -
1.4 Salinity. -
1.4.1 Salinity and electrical conductivity. -
1.4.2 The practical salinity scale. -
1.4.3 Nonconductive methods. -
1.5 Depth or pressure. -
1.5.1 Hydrostatic pressure. -
1.5.2 Free-fall velocity. -
1.5.3 Echo sounding. -
1.5.4 Other depth sounding methods. -
1.6 Sea-level measurement. -
1.6.1 Tide and pressure gauges. -
1.6.2 Satellite altimetry. -
1.6.3 Inverted echo sounder (IES). -
1.6.4 Wave height and direction. -
1.7 Eulerian currents. -
1.7.1 Early current meter technology. -
1.7.2 Rotor-type current meters. -
1.7.3 Nonmechanical current meters. -
1.7.4 Profiling acoustic Doppler current meters (ADCM). -
1.7.5 Comparisons of current meters. -
1.7.6 Electromagnetic methods. -
1.7.7 Other methods of current measurement. -
1.7.8 Mooring logistics. -
1.7.9 Acoustic releases. -
1.8 Lagrangian current measurements. -
1.8.1 Drift cards and bottles. -
1.8.2 Modern drifters. -
1.8.3 Processing satellite-tracked drifter data. -
1.8.4 Drifter response. -
1.8.5 Other types of surface drifters. -
1.8.6 Subsurface floats. -
1.8.7 Surface displacements in satellite imagery. -
1.9 Wind. -
1.10 Precipitation. -
1.11 Chemical tracers. -
1.11.1 Conventional tracers. -
1.11.2 Light attenuation and scattering. -
1.11.3 Oxygen isotope: δ18O. -
1.11.4 Helium-3; helium/heat ratio. -
1.12 Transient chemical tracers. -
1.12.1 Tritium. -
1.12.2 Radiocarbon. -
1.12.3 Chlorofluorocarbons. -
1.12.4 Radon-222. -
1.12.5 Sulfur hexachloride. -
1.12.6 Strontium-90. -
Chapter 2 Data Processing and Presentation. -
2.1 Introduction. -
2.2 Calibration. -
2.3 Interpolation. -
2.4 Data presentation. -
2.4.1 Introduction. -
2.4.2 Vertical profiles. -
2.4.3 Vertical sections. -
2.4.4 Horizontal maps. -
2.4.5 Map projections. -
2.4.6 Characteristic or property versus property diagrams. -
2.4.7 Time-series presentation. -
2.4.8 Histograms. -
2.4.9 New directions in graphical presentation. -
Chapter 3 Statistical Methods and Error Handling. -
3.1 Introduction. -
3.2 Sample distributions. -
3.3 Probability. -
3.3.1 Cumulative probability functions. -
3.4 Moments and expected values. -
3.4.1 Unbiased estimators and moments. -
3.4.2 Moment generating functions. -
3.5 Common probability density functions. -
3.6 Central limit theorem. -
3.7 Estimation. -
3.8 Confidence intervals. -
3.8.1 Confidence interval for μ (σ known)
3.8.2 Confidence interval for μ (σ unknown)
3.8.3 Confidence interval for σ^2. -
3.8.4 Goodness-of-fit test. -
3.9 Selecting the sample size. -
3.10 Confidence intervals for altimeter bias estimates. -
3.11 Estimation methods. -
3.11.1 Minimum variance unbiased estimation. -
3.11.2 Method of moments. -
3.11.3 Maximum likelihood. -
3.12 Linear estimation (regression). -
3.12.1 Method of least squares. -
3.12.2 Standard error of the estimate. -
3.12.3 Multivariate regression. -
3.12.4 A computational example of matrix regression. -
3.12.5 Polynomial curve fitting with least squares. -
3.12.6 Relationship between least-squares and maximum likelihood. -
3.13 Relationship between regression and correlation. -
3.13.1 The effects of random errors on correlation. -
3.13.2 The maximum likelihood correlation estimator. -
3.13.3 Correlation and regression: cause and effect. -
3.14 Hypothesis testing. -
3.14.1 Significance levels and confidence intervals for correlation. -
3.14.2 Analysis of variance and the F-distribution. -
3.15 Effective degrees of freedom. -
3.1 5.1 Trend estimates and the integral time scale. -
3.16 Editing and despiking techniques: the nature of errors. -
3.16.1 Identifying and removing errors. -
3.16.2 Propagation of error. -
3.16.3 Dealing with numbers: the statistics of roundoff. -
3.16.4 Gauss-Markov theorem. -
3.17 Interpolation: filling the data gaps. -
3.17.1 Equally and unequally spaced data. -
3.17.2 Interpolation methods. -
3.17.3 Interpolating gappy records: practical examples. -
3.18 Covariance and the covariance matrix. -
3.18.1 Covariance and structure functions. -
3.18.2 A computational example. -
3.18.3 Multivariate distributions. -
3.19 Bootstrap and jackknife methods. -
3.19.1 Bootstrap method. -
3.19.2 Jackknife method. -
Chapter 4 The Spatial Analyses of Data Fields. -
4.1 Traditional block and bulk averaging. -
4.2 Objective analysis. -
4.2.1 Objective mapping: examples. -
4.3 Empirical orthogonal functions. -
4.3.1 Principal axes of a single vector time series (scatter plot). -
4.3.2 EOF computation using the scatter matrix method. -
4.3.3 EOF computation using singular value decomposition. -
4.3.4 An example: deep currents near a mid-ocean ridge. -
4.3.S Interpretation of EOFs. -
4.3.6 Variations on conventional EOF analysis. -
4.4 Normal mode analysis. -
4.4.1 Vertical normal modes. -
4.4.2 An example: normal modes of semidiurnal frequency. -
4.4.3 Coastal-trapped waves (CTWs). -
4.5 Inverse methods. -
4.5.1 General inverse theory. -
4.5.2 Inverse theory and absolute currents. -
4.5.3 The IWEX internal wave problem. -
4.5.4 Summary of inverse methods. -
Chapter 5 Time-series Analysis Methods. -
5.1 Basic concepts. -
5.2 Stochastic processes and stationarity. -
5.3 Correlation functions. -
5.4 Fourier analysis. -
5.4.1 Mathematical formulation. -
5.4.2 Discrete time series. -
5.4.3 A computational example. -
5.4.4 Fourier analysis for specified frequencies. -
5.4.5 The fast Fourier transform. -
5.5 Harmonic analysis. -
5.5.1 A least-squares method. -
5.5.2 A computational example. -
5.5.3 Harmonic analysis of tides. -
5.5.4 Choice of constituents. -
5.5.5 A computational example for tides. -
5.5.6 Complex demodulation. -
5.6 Spectral analysis. -
5.6.1 Spectra of deterministic and stochastic processes. -
5.6.2 Spectra of discrete series. -
5.6.3 Conventional spectral methods. -
5.6.4 Spectra of vector series. -
5.6.5 Effect of sampling on spectral estimates. -
5.6.6 Smoothing spectral estimates (windowing). -
5.6.7 Smoothing spectra in the frequency domain. -
5.6.8 Confidence intervals on spectra. -
5.6.9 Zero-padding and prewhitening. -
5.6.10 Spectral analysis of unevenly spaced time series. -
5.6.11 General spectral bandwidth and Q of the system. -
5.6.12 Summary of the standard spectral analysis approach. -
5.7 Spectral analysis (parametric methods). -
5.7.1 Some basic concepts. -
5.7.2 Autoregressive power spectral estimation. -
5.7.3 Maximum likelihood spectral estimation. -
5.8 Cross-spectral analysis. -
5.8.1 Cross-correlation functions. -
5.8.2 Cross-covariance method. -
5.8.3 Fourier transform method. -
5.8.4 Phase and cross-amplitude functions. -
5.8.S Coincident and quadrature spectra. -
5.8.6 Coherence spectrum (coherency). -
5.8.7 Frequency response of a linear system. -
5.8.8 Rotary cross-spectral analysis. -
5.9 Wavelet analysis. -
5.9.1 The wavelet transform. -
5.9.2 Wavelet algorithms. -
5.9.3 Oceanographic examples. -
5.9.4 The S-transformation. -
5.9.5 The multiple filter technique. -
5.10 Digital filters. -
5.10.1 Introduction. -
5.10.2 Basic concepts. -
5.10.3 Ideal filters. -
Location:
AWI Reading room
Branch Library:
AWI Library
Permalink
