Principal Component Analysis in Meteorology and Oceanography PDF Download

Author: Rudolph W. Preisendorfer
Publisher:
ISBN:
Category : Climatology
Languages : en
Pages : 452

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Author: I.T. Jolliffe
Publisher: Springer Science & Business Media
ISBN: 0387224408
Category : Mathematics
Languages : en
Pages : 513

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Book Description
The first edition of this book was the first comprehensive text written solely on principal component analysis. The second edition updates and substantially expands the original version, and is once again the definitive text on the subject. It includes core material, current research and a wide range of applications. Its length is nearly double that of the first edition.

Author: I.T. Jolliffe
Publisher: Springer Science & Business Media
ISBN: 1475719043
Category : Mathematics
Languages : en
Pages : 283

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Book Description
Principal component analysis is probably the oldest and best known of the It was first introduced by Pearson (1901), techniques ofmultivariate analysis. and developed independently by Hotelling (1933). Like many multivariate methods, it was not widely used until the advent of electronic computers, but it is now weIl entrenched in virtually every statistical computer package. The central idea of principal component analysis is to reduce the dimen sionality of a data set in which there are a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. This reduction is achieved by transforming to a new set of variables, the principal components, which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all of the original variables. Computation of the principal components reduces to the solution of an eigenvalue-eigenvector problem for a positive-semidefinite symmetrie matrix. Thus, the definition and computation of principal components are straightforward but, as will be seen, this apparently simple technique has a wide variety of different applications, as weIl as a number of different deri vations. Any feelings that principal component analysis is a narrow subject should soon be dispelled by the present book; indeed some quite broad topics which are related to principal component analysis receive no more than a brief mention in the final two chapters.

Author: Daniel S. Wilks
Publisher: Elsevier
ISBN: 0080541720
Category : Science
Languages : en
Pages : 481

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Book Description
This book introduces and explains the statistical methods used to describe, analyze, test, and forecast atmospheric data. It will be useful to students, scientists, and other professionals who seek to make sense of the scientific literature in meteorology, climatology, or other geophysical disciplines, or to understand and communicate what their atmospheric data sets have to say. The book includes chapters on exploratory data analysis, probability distributions, hypothesis testing, statistical weather forecasting, forecast verification, time(series analysis, and multivariate data analysis. Worked examples, exercises, and illustrations facilitate understanding of the material; an extensive and up-to-date list of references allows the reader to pursue selected topics in greater depth. Key Features* Presents and explains techniques used in atmospheric data summarization, analysis, testing, and forecasting* Includes extensive and up-to-date references* Features numerous worked examples and exercises* Contains over 130 illustrations

Author: David M. Glover
Publisher: Cambridge University Press
ISBN: 1139500716
Category : Science
Languages : en
Pages : 589

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Book Description
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.

Author: Frank L. Estis
Publisher:
ISBN:
Category : Atmospheric pressure
Languages : en
Pages : 150

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Book Description
The purpose of this study was to identify preferred areas of synoptic scale disturbance activity through use of the objective analysis technique called complex principal component analysis. The technique was applied to a sea level pressure data set consisting of twice daily observations (00 and 12 GMT) during the months of January, February, and March, for the years 1973-1976 and 1979-1982. These months were chosen to encompass the time frame of the Genesis of Atlantic Lows Experiment 1986. The geographical area studied extended approximately from 30-120 deg west and from 25-60 deg north. The complex principal component analysis technique allows the detection of propagating features in the pressure data set. The original data set is augmented with its own Hilbert transform, which is nearly equivalent in the time domain to the quadrature spectrum in the frequency domain. Variance in the pressure field is felt to be a direct reflection of disturbance activity. Furthermore, when the data are filtered to a 'synoptic scale' frequency window, variance in the pressure field corresponds closely with cyclone and anticyclone storm tracks over a period of two to ten days. Results of this analysis produced four components that could be given a preliminary interpretation from a physical sense.

Author: Timothy DelSole
Publisher: Cambridge University Press
ISBN: 1108472419
Category : Mathematics
Languages : en
Pages : 545

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An accessible introduction to statistical methods for students in the climate sciences.

Author: Willi Freeden
Publisher: Springer Science & Business Media
ISBN: 364201545X
Category : Mathematics
Languages : en
Pages : 1371

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Book Description
During the last three decades geosciences and geo-engineering were influenced by two essential scenarios: First, the technological progress has changed completely the observational and measurement techniques. Modern high speed computers and satellite based techniques are entering more and more all geodisciplines. Second, there is a growing public concern about the future of our planet, its climate, its environment, and about an expected shortage of natural resources. Obviously, both aspects, viz. efficient strategies of protection against threats of a changing Earth and the exceptional situation of getting terrestrial, airborne as well as spaceborne data of better and better quality explain the strong need of new mathematical structures, tools, and methods. Mathematics concerned with geoscientific problems, i.e., Geomathematics, is becoming increasingly important. The ‘Handbook Geomathematics’ as a central reference work in this area comprises the following scientific fields: (I) observational and measurement key technologies (II) modelling of the system Earth (geosphere, cryosphere, hydrosphere, atmosphere, biosphere) (III) analytic, algebraic, and operator-theoretic methods (IV) statistical and stochastic methods (V) computational and numerical analysis methods (VI) historical background and future perspectives.

Author: Richard E. Thomson
Publisher: Elsevier
ISBN: 0323993133
Category : Science
Languages : en
Pages : 892

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Book Description
Data Analysis Methods in Physical Oceanography, Fourth Edition provides a practical reference to established and modern data analysis techniques in earth and ocean sciences. In five sections, the book addresses data acquisition and recording, data processing and presentation, statistical methods and error handling, analysis of spatial data fields, and time series analysis methods. The updated edition includes new information on autonomous platforms and new analysis tools such as “deep learning and convolutional neural networks. A section on extreme value statistics has been added, and the section on wavelet analysis has been expanded. This book brings together relevant techniques and references recent papers where these techniques have been trialed. In addition, it presents valuable examples using physical oceanography data. For students, the sections on data acquisition are useful for a compilation of all the measurement methods. Includes content co-authored by scientists from academia and industry, both of whom have more than 30 years of experience in oceanographic research and field work Provides boxed worked examples that address typical data analysis problems, including examples with computer code (e.g., python code, MATLAB code) Presents brief summaries at the end of the more difficult sections to help readers looking for foundational information

Author: Wendy Hui Kyong Chun
Publisher: MIT Press
ISBN: 0262046229
Category : Technology & Engineering
Languages : en
Pages : 341

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Book Description
How big data and machine learning encode discrimination and create agitated clusters of comforting rage. In Discriminating Data, Wendy Hui Kyong Chun reveals how polarization is a goal—not an error—within big data and machine learning. These methods, she argues, encode segregation, eugenics, and identity politics through their default assumptions and conditions. Correlation, which grounds big data’s predictive potential, stems from twentieth-century eugenic attempts to “breed” a better future. Recommender systems foster angry clusters of sameness through homophily. Users are “trained” to become authentically predictable via a politics and technology of recognition. Machine learning and data analytics thus seek to disrupt the future by making disruption impossible. Chun, who has a background in systems design engineering as well as media studies and cultural theory, explains that although machine learning algorithms may not officially include race as a category, they embed whiteness as a default. Facial recognition technology, for example, relies on the faces of Hollywood celebrities and university undergraduates—groups not famous for their diversity. Homophily emerged as a concept to describe white U.S. resident attitudes to living in biracial yet segregated public housing. Predictive policing technology deploys models trained on studies of predominantly underserved neighborhoods. Trained on selected and often discriminatory or dirty data, these algorithms are only validated if they mirror this data. How can we release ourselves from the vice-like grip of discriminatory data? Chun calls for alternative algorithms, defaults, and interdisciplinary coalitions in order to desegregate networks and foster a more democratic big data.