國立虎尾科技大學 |

Patterns Identification and Data Mining in Weather and Climate

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Patterns Identification and Data Mining in Weather and Climate/ by Abdelwaheb Hannachi.
作者:	Hannachi, Abdelwaheb.
面頁冊數:	XXIV, 600 p. 201 illus., 79 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Environment, general. -
電子資源:	https://doi.org/10.1007/978-3-030-67073-3
ISBN:	9783030670733

Patterns Identification and Data Mining in Weather and Climate
Hannachi, Abdelwaheb.

Patterns Identification and Data Mining in Weather and Climate[electronic resource] /by Abdelwaheb Hannachi. - 1st ed. 2021. - XXIV, 600 p. 201 illus., 79 illus. in color.online resource. - Springer Atmospheric Sciences,2194-5217. - Springer Atmospheric Sciences,.

Chapter 1 Introduction -- Chapter 2 General Setting and Basic Terminology -- Chapter 3 Empirical Orthogonal Functions -- Chapter 4 Rotated and Simplified EOFs -- Chapter 5 Complex/Hilbert EOFs -- Chapter 6 Principal Oscillation Patterns and their extension -- Chapter 7 Extended EOFs and SSA -- Chapter 8 Persistent, Predictive, and Interpolated Patterns -- Chapter 9 Principal Coordinates or Multidimensional Scaling -- Chapter 10 Factor Analysis -- Chapter 11 Projection Pursuit -- Chapter 12 Independent Component Analysis -- Chapter 13 Kernel EOFs -- Chapter 14 Functional and Regularised EOFs -- Chapter 15 Methods for Coupled Patterns -- Chapter 16 Further topics -- Chapter 17 Machine Learning -- Appendix A Smoothing Techniques -- Appendix B Introduction to Probability and Random Variables.-Appendix C Stationary Time Series Analysis.-Appendix D Matrix Algebra and Matrix Function -- Appendix E Optimisation Algorithms -- Appendix F Hilbert Space -- Appendix G Systems of Linear Ordinary Differential Equations -- Appendix H Links for Software Resource Material -- Index.

Advances in computer power and observing systems has led to the generation and accumulation of large scale weather & climate data begging for exploration and analysis. Pattern Identification and Data Mining in Weather and Climate presents, from different perspectives, most available, novel and conventional, approaches used to analyze multivariate time series in climate science to identify patterns of variability, teleconnections, and reduce dimensionality. The book discusses different methods to identify patterns of spatiotemporal fields. The book also presents machine learning with a particular focus on the main methods used in climate science. Applications to atmospheric and oceanographic data are also presented and discussed in most chapters. To help guide students and beginners in the field of weather & climate data analysis, basic Matlab skeleton codes are given is some chapters, complemented with a list of software links toward the end of the text. A number of technical appendices are also provided, making the text particularly suitable for didactic purposes. The topic of EOFs and associated pattern identification in space-time data sets has gone through an extraordinary fast development, both in terms of new insights and the breadth of applications. We welcome this text by Abdel Hannachi who not only has a deep insight in the field but has himself made several contributions to new developments in the last 15 years. - Huug van den Dool, Climate Prediction Center, NCEP, College Park, MD, U.S.A. Now that weather and climate science is producing ever larger and richer data sets, the topic of pattern extraction and interpretation has become an essential part. This book provides an up to date overview of the latest techniques and developments in this area. - Maarten Ambaum, Department of Meteorology, University of Reading, U.K. This nicely and expertly written book covers a lot of ground, ranging from classical linear pattern identification techniques to more modern machine learning, illustrated with examples from weather & climate science. It will be very valuable both as a tutorial for graduate and postgraduate students and as a reference text for researchers and practitioners in the field. - Frank Kwasniok, College of Engineering, University of Exeter, U.K.

ISBN: 9783030670733

Standard No.: 10.1007/978-3-030-67073-3doiSubjects--Topical Terms:

668450
Environment, general.

LC Class. No.: QC851-999

Dewey Class. No.: 551.5

Patterns Identification and Data Mining in Weather and Climate
LDR:04824nam a22004095i 4500 001 1053795
003 DE-He213
005 20210619042127.0
007 cr nn 008mamaa
008 220103s2021 gw | s |||| 0|eng d
020 $a 9783030670733 $9 978-3-030-67073-3
024 7 $a 10.1007/978-3-030-67073-3 $2 doi
035 $a 978-3-030-67073-3
050 4 $a QC851-999
072 7 $a RB $2 bicssc
072 7 $a SCI042000 $2 bisacsh
072 7 $a RB $2 thema
082 0 4 $a 551.5 $2 23
100 1 $a Hannachi, Abdelwaheb. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1358732
245 1 0 $a Patterns Identification and Data Mining in Weather and Climate $h [electronic resource] / $c by Abdelwaheb Hannachi.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a XXIV, 600 p. 201 illus., 79 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Springer Atmospheric Sciences, $x 2194-5217
505 0 $a Chapter 1 Introduction -- Chapter 2 General Setting and Basic Terminology -- Chapter 3 Empirical Orthogonal Functions -- Chapter 4 Rotated and Simplified EOFs -- Chapter 5 Complex/Hilbert EOFs -- Chapter 6 Principal Oscillation Patterns and their extension -- Chapter 7 Extended EOFs and SSA -- Chapter 8 Persistent, Predictive, and Interpolated Patterns -- Chapter 9 Principal Coordinates or Multidimensional Scaling -- Chapter 10 Factor Analysis -- Chapter 11 Projection Pursuit -- Chapter 12 Independent Component Analysis -- Chapter 13 Kernel EOFs -- Chapter 14 Functional and Regularised EOFs -- Chapter 15 Methods for Coupled Patterns -- Chapter 16 Further topics -- Chapter 17 Machine Learning -- Appendix A Smoothing Techniques -- Appendix B Introduction to Probability and Random Variables.-Appendix C Stationary Time Series Analysis.-Appendix D Matrix Algebra and Matrix Function -- Appendix E Optimisation Algorithms -- Appendix F Hilbert Space -- Appendix G Systems of Linear Ordinary Differential Equations -- Appendix H Links for Software Resource Material -- Index.
520 $a Advances in computer power and observing systems has led to the generation and accumulation of large scale weather & climate data begging for exploration and analysis. Pattern Identification and Data Mining in Weather and Climate presents, from different perspectives, most available, novel and conventional, approaches used to analyze multivariate time series in climate science to identify patterns of variability, teleconnections, and reduce dimensionality. The book discusses different methods to identify patterns of spatiotemporal fields. The book also presents machine learning with a particular focus on the main methods used in climate science. Applications to atmospheric and oceanographic data are also presented and discussed in most chapters. To help guide students and beginners in the field of weather & climate data analysis, basic Matlab skeleton codes are given is some chapters, complemented with a list of software links toward the end of the text. A number of technical appendices are also provided, making the text particularly suitable for didactic purposes. The topic of EOFs and associated pattern identification in space-time data sets has gone through an extraordinary fast development, both in terms of new insights and the breadth of applications. We welcome this text by Abdel Hannachi who not only has a deep insight in the field but has himself made several contributions to new developments in the last 15 years. - Huug van den Dool, Climate Prediction Center, NCEP, College Park, MD, U.S.A. Now that weather and climate science is producing ever larger and richer data sets, the topic of pattern extraction and interpretation has become an essential part. This book provides an up to date overview of the latest techniques and developments in this area. - Maarten Ambaum, Department of Meteorology, University of Reading, U.K. This nicely and expertly written book covers a lot of ground, ranging from classical linear pattern identification techniques to more modern machine learning, illustrated with examples from weather & climate science. It will be very valuable both as a tutorial for graduate and postgraduate students and as a reference text for researchers and practitioners in the field. - Frank Kwasniok, College of Engineering, University of Exeter, U.K.
650 2 4 $a Environment, general. $3 668450
650 2 4 $a Pattern Recognition. $3 669796
650 2 4 $a Climate, general. $3 1023469
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 1 4 $a Atmospheric Sciences. $3 881331
650 0 $a Environment. $3 579342
650 0 $a Pattern recognition. $3 1253525
650 0 $a Climate. $3 1256884
650 0 $a Data mining. $3 528622
650 0 $a Atmospheric sciences. $3 1179392
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030670726
776 0 8 $i Printed edition: $z 9783030670740
776 0 8 $i Printed edition: $z 9783030670757
830 0 $a Springer Atmospheric Sciences, $x 2194-5217 $3 1254668
856 4 0 $u https://doi.org/10.1007/978-3-030-67073-3
912 $a ZDB-2-EES
912 $a ZDB-2-SXEE
950 $a Earth and Environmental Science (SpringerNature-11646)
950 $a Earth and Environmental Science (R0) (SpringerNature-43711)