Dynamic data assimilation: a least squares approach
Dynamic data assimilation is the assessment, combination and synthesis of observational data, scientific laws and mathematical models to determine the state of a complex physical system, for instance as a preliminary step in making predictions about the system's behaviour. The topic has assumed...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2006
|
| Schriftenreihe: | Encyclopedia of mathematics and its applications
volume 104 |
| Schlagworte: | |
| Online-Zugang: | BSB01 FHN01 Volltext |
| Zusammenfassung: | Dynamic data assimilation is the assessment, combination and synthesis of observational data, scientific laws and mathematical models to determine the state of a complex physical system, for instance as a preliminary step in making predictions about the system's behaviour. The topic has assumed increasing importance in fields such as numerical weather prediction where conscientious efforts are being made to extend the term of reliable weather forecasts beyond the few days that are presently feasible. This book is designed to be a basic one-stop reference for graduate students and researchers. It is based on graduate courses taught over a decade to mathematicians, scientists, and engineers, and its modular structure accommodates the various audience requirements. Thus Part I is a broad introduction to the history, development and philosophy of data assimilation, illustrated by examples; Part II considers the classical, static approaches, both linear and nonlinear; and Part III describes computational techniques. Parts IV to VII are concerned with how statistical and dynamic ideas can be incorporated into the classical framework. Key themes covered here include estimation theory, stochastic and dynamic models, and sequential filtering. The final part addresses the predictability of dynamical systems. Chapters end with a section that provides pointers to the literature, and a set of exercises with instructive hints |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Beschreibung: | 1 online resource (xxii, 654 pages) |
| ISBN: | 9780511526480 |
| DOI: | 10.1017/CBO9780511526480 |
Internformat
MARC
| LEADER | 00000nmm a2200000zcb4500 | ||
|---|---|---|---|
| 001 | BV043942109 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 161206s2006 |||| o||u| ||||||eng d | ||
| 020 | |a 9780511526480 |c Online |9 978-0-511-52648-0 | ||
| 024 | 7 | |a 10.1017/CBO9780511526480 |2 doi | |
| 035 | |a (ZDB-20-CBO)CR9780511526480 | ||
| 035 | |a (OCoLC)850574866 | ||
| 035 | |a (DE-599)BVBBV043942109 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-12 |a DE-92 | ||
| 082 | 0 | |a 511.8 |2 22 | |
| 100 | 1 | |a Lewis, J. M. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Dynamic data assimilation |b a least squares approach |c John M. Lewis, S. Lakshmivarahan, Sudarshan Dhall |
| 264 | 1 | |a Cambridge |b Cambridge University Press |c 2006 | |
| 300 | |a 1 online resource (xxii, 654 pages) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a Encyclopedia of mathematics and its applications |v volume 104 | |
| 500 | |a Title from publisher's bibliographic system (viewed on 05 Oct 2015) | ||
| 505 | 8 | 0 | |g 1 |t Synopsis |g 2 |t Pathways into data assimilation : illustrative examples |g 3 |t Applications |g 4 |t Brief history of data assimilation |g 5 |t Linear least squares estimation : method of normal equations |g 6 |t A geometric view : projection and invariance |g 7 |t Nonlinear least squares estimation |g 8 |t Recursive least squares estimation |g 9 |t Matrix methods |g 10 |t Optimization : steepest descent method |g 11 |t Conjugate direction/gradient methods |g 12 |t Newton and quasi-Newton methods |g 13 |t Principles of statistical estimation |g 14 |t Statistical least squares estimation |g 15 |t Maximum likelihood method |g 16 |t Bayesian estimation method |g 17 |t From Gauss to Kalman : sequential, linear minimum variance estimation |
| 520 | |a Dynamic data assimilation is the assessment, combination and synthesis of observational data, scientific laws and mathematical models to determine the state of a complex physical system, for instance as a preliminary step in making predictions about the system's behaviour. The topic has assumed increasing importance in fields such as numerical weather prediction where conscientious efforts are being made to extend the term of reliable weather forecasts beyond the few days that are presently feasible. This book is designed to be a basic one-stop reference for graduate students and researchers. It is based on graduate courses taught over a decade to mathematicians, scientists, and engineers, and its modular structure accommodates the various audience requirements. Thus Part I is a broad introduction to the history, development and philosophy of data assimilation, illustrated by examples; Part II considers the classical, static approaches, both linear and nonlinear; and Part III describes computational techniques. Parts IV to VII are concerned with how statistical and dynamic ideas can be incorporated into the classical framework. Key themes covered here include estimation theory, stochastic and dynamic models, and sequential filtering. The final part addresses the predictability of dynamical systems. Chapters end with a section that provides pointers to the literature, and a set of exercises with instructive hints | ||
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Simulation methods | |
| 650 | 4 | |a Mathematical models | |
| 700 | 1 | |a Lakshmivarahan, S. |e Sonstige |4 oth | |
| 700 | 1 | |a Dhall, Sudarshan Kumar |d 1937- |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druckausgabe |z 978-0-521-85155-8 |
| 856 | 4 | 0 | |u https://doi.org/10.1017/CBO9780511526480 |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
| 912 | |a ZDB-20-CBO | ||
| 999 | |a oai:aleph.bib-bvb.de:BVB01-029351079 | ||
| 966 | e | |u https://doi.org/10.1017/CBO9780511526480 |l BSB01 |p ZDB-20-CBO |q BSB_PDA_CBO |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1017/CBO9780511526480 |l FHN01 |p ZDB-20-CBO |q FHN_PDA_CBO |x Verlag |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1804176884570783744 |
|---|---|
| any_adam_object | |
| author | Lewis, J. M. |
| author_facet | Lewis, J. M. |
| author_role | aut |
| author_sort | Lewis, J. M. |
| author_variant | j m l jm jml |
| building | Verbundindex |
| bvnumber | BV043942109 |
| collection | ZDB-20-CBO |
| contents | Synopsis Pathways into data assimilation : illustrative examples Applications Brief history of data assimilation Linear least squares estimation : method of normal equations A geometric view : projection and invariance Nonlinear least squares estimation Recursive least squares estimation Matrix methods Optimization : steepest descent method Conjugate direction/gradient methods Newton and quasi-Newton methods Principles of statistical estimation Statistical least squares estimation Maximum likelihood method Bayesian estimation method From Gauss to Kalman : sequential, linear minimum variance estimation |
| ctrlnum | (ZDB-20-CBO)CR9780511526480 (OCoLC)850574866 (DE-599)BVBBV043942109 |
| dewey-full | 511.8 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 511 - General principles of mathematics |
| dewey-raw | 511.8 |
| dewey-search | 511.8 |
| dewey-sort | 3511.8 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1017/CBO9780511526480 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03873nmm a2200457zcb4500</leader><controlfield tag="001">BV043942109</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">161206s2006 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780511526480</subfield><subfield code="c">Online</subfield><subfield code="9">978-0-511-52648-0</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1017/CBO9780511526480</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-20-CBO)CR9780511526480</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)850574866</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV043942109</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-12</subfield><subfield code="a">DE-92</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">511.8</subfield><subfield code="2">22</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lewis, J. M.</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Dynamic data assimilation</subfield><subfield code="b">a least squares approach</subfield><subfield code="c">John M. Lewis, S. Lakshmivarahan, Sudarshan Dhall</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cambridge</subfield><subfield code="b">Cambridge University Press</subfield><subfield code="c">2006</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (xxii, 654 pages)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Encyclopedia of mathematics and its applications</subfield><subfield code="v">volume 104</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Title from publisher's bibliographic system (viewed on 05 Oct 2015)</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="g">1</subfield><subfield code="t">Synopsis</subfield><subfield code="g">2</subfield><subfield code="t">Pathways into data assimilation : illustrative examples</subfield><subfield code="g">3</subfield><subfield code="t">Applications</subfield><subfield code="g">4</subfield><subfield code="t">Brief history of data assimilation</subfield><subfield code="g">5</subfield><subfield code="t">Linear least squares estimation : method of normal equations</subfield><subfield code="g">6</subfield><subfield code="t">A geometric view : projection and invariance</subfield><subfield code="g">7</subfield><subfield code="t">Nonlinear least squares estimation</subfield><subfield code="g">8</subfield><subfield code="t">Recursive least squares estimation</subfield><subfield code="g">9</subfield><subfield code="t">Matrix methods</subfield><subfield code="g">10</subfield><subfield code="t">Optimization : steepest descent method</subfield><subfield code="g">11</subfield><subfield code="t">Conjugate direction/gradient methods</subfield><subfield code="g">12</subfield><subfield code="t">Newton and quasi-Newton methods</subfield><subfield code="g">13</subfield><subfield code="t">Principles of statistical estimation</subfield><subfield code="g">14</subfield><subfield code="t">Statistical least squares estimation</subfield><subfield code="g">15</subfield><subfield code="t">Maximum likelihood method</subfield><subfield code="g">16</subfield><subfield code="t">Bayesian estimation method</subfield><subfield code="g">17</subfield><subfield code="t">From Gauss to Kalman : sequential, linear minimum variance estimation</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Dynamic data assimilation is the assessment, combination and synthesis of observational data, scientific laws and mathematical models to determine the state of a complex physical system, for instance as a preliminary step in making predictions about the system's behaviour. The topic has assumed increasing importance in fields such as numerical weather prediction where conscientious efforts are being made to extend the term of reliable weather forecasts beyond the few days that are presently feasible. This book is designed to be a basic one-stop reference for graduate students and researchers. It is based on graduate courses taught over a decade to mathematicians, scientists, and engineers, and its modular structure accommodates the various audience requirements. Thus Part I is a broad introduction to the history, development and philosophy of data assimilation, illustrated by examples; Part II considers the classical, static approaches, both linear and nonlinear; and Part III describes computational techniques. Parts IV to VII are concerned with how statistical and dynamic ideas can be incorporated into the classical framework. Key themes covered here include estimation theory, stochastic and dynamic models, and sequential filtering. The final part addresses the predictability of dynamical systems. Chapters end with a section that provides pointers to the literature, and a set of exercises with instructive hints</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematisches Modell</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Simulation methods</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematical models</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lakshmivarahan, S.</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dhall, Sudarshan Kumar</subfield><subfield code="d">1937-</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druckausgabe</subfield><subfield code="z">978-0-521-85155-8</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1017/CBO9780511526480</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-20-CBO</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-029351079</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/CBO9780511526480</subfield><subfield code="l">BSB01</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">BSB_PDA_CBO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/CBO9780511526480</subfield><subfield code="l">FHN01</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">FHN_PDA_CBO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV043942109 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:39:17Z |
| institution | BVB |
| isbn | 9780511526480 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029351079 |
| oclc_num | 850574866 |
| open_access_boolean | |
| owner | DE-12 DE-92 |
| owner_facet | DE-12 DE-92 |
| physical | 1 online resource (xxii, 654 pages) |
| psigel | ZDB-20-CBO ZDB-20-CBO BSB_PDA_CBO ZDB-20-CBO FHN_PDA_CBO |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Cambridge University Press |
| record_format | marc |
| series2 | Encyclopedia of mathematics and its applications |
| spelling | Lewis, J. M. Verfasser aut Dynamic data assimilation a least squares approach John M. Lewis, S. Lakshmivarahan, Sudarshan Dhall Cambridge Cambridge University Press 2006 1 online resource (xxii, 654 pages) txt rdacontent c rdamedia cr rdacarrier Encyclopedia of mathematics and its applications volume 104 Title from publisher's bibliographic system (viewed on 05 Oct 2015) 1 Synopsis 2 Pathways into data assimilation : illustrative examples 3 Applications 4 Brief history of data assimilation 5 Linear least squares estimation : method of normal equations 6 A geometric view : projection and invariance 7 Nonlinear least squares estimation 8 Recursive least squares estimation 9 Matrix methods 10 Optimization : steepest descent method 11 Conjugate direction/gradient methods 12 Newton and quasi-Newton methods 13 Principles of statistical estimation 14 Statistical least squares estimation 15 Maximum likelihood method 16 Bayesian estimation method 17 From Gauss to Kalman : sequential, linear minimum variance estimation Dynamic data assimilation is the assessment, combination and synthesis of observational data, scientific laws and mathematical models to determine the state of a complex physical system, for instance as a preliminary step in making predictions about the system's behaviour. The topic has assumed increasing importance in fields such as numerical weather prediction where conscientious efforts are being made to extend the term of reliable weather forecasts beyond the few days that are presently feasible. This book is designed to be a basic one-stop reference for graduate students and researchers. It is based on graduate courses taught over a decade to mathematicians, scientists, and engineers, and its modular structure accommodates the various audience requirements. Thus Part I is a broad introduction to the history, development and philosophy of data assimilation, illustrated by examples; Part II considers the classical, static approaches, both linear and nonlinear; and Part III describes computational techniques. Parts IV to VII are concerned with how statistical and dynamic ideas can be incorporated into the classical framework. Key themes covered here include estimation theory, stochastic and dynamic models, and sequential filtering. The final part addresses the predictability of dynamical systems. Chapters end with a section that provides pointers to the literature, and a set of exercises with instructive hints Mathematisches Modell Simulation methods Mathematical models Lakshmivarahan, S. Sonstige oth Dhall, Sudarshan Kumar 1937- Sonstige oth Erscheint auch als Druckausgabe 978-0-521-85155-8 https://doi.org/10.1017/CBO9780511526480 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Lewis, J. M. Dynamic data assimilation a least squares approach Synopsis Pathways into data assimilation : illustrative examples Applications Brief history of data assimilation Linear least squares estimation : method of normal equations A geometric view : projection and invariance Nonlinear least squares estimation Recursive least squares estimation Matrix methods Optimization : steepest descent method Conjugate direction/gradient methods Newton and quasi-Newton methods Principles of statistical estimation Statistical least squares estimation Maximum likelihood method Bayesian estimation method From Gauss to Kalman : sequential, linear minimum variance estimation Mathematisches Modell Simulation methods Mathematical models |
| title | Dynamic data assimilation a least squares approach |
| title_alt | Synopsis Pathways into data assimilation : illustrative examples Applications Brief history of data assimilation Linear least squares estimation : method of normal equations A geometric view : projection and invariance Nonlinear least squares estimation Recursive least squares estimation Matrix methods Optimization : steepest descent method Conjugate direction/gradient methods Newton and quasi-Newton methods Principles of statistical estimation Statistical least squares estimation Maximum likelihood method Bayesian estimation method From Gauss to Kalman : sequential, linear minimum variance estimation |
| title_auth | Dynamic data assimilation a least squares approach |
| title_exact_search | Dynamic data assimilation a least squares approach |
| title_full | Dynamic data assimilation a least squares approach John M. Lewis, S. Lakshmivarahan, Sudarshan Dhall |
| title_fullStr | Dynamic data assimilation a least squares approach John M. Lewis, S. Lakshmivarahan, Sudarshan Dhall |
| title_full_unstemmed | Dynamic data assimilation a least squares approach John M. Lewis, S. Lakshmivarahan, Sudarshan Dhall |
| title_short | Dynamic data assimilation |
| title_sort | dynamic data assimilation a least squares approach |
| title_sub | a least squares approach |
| topic | Mathematisches Modell Simulation methods Mathematical models |
| topic_facet | Mathematisches Modell Simulation methods Mathematical models |
| url | https://doi.org/10.1017/CBO9780511526480 |
| work_keys_str_mv | AT lewisjm dynamicdataassimilationaleastsquaresapproach AT lakshmivarahans dynamicdataassimilationaleastsquaresapproach AT dhallsudarshankumar dynamicdataassimilationaleastsquaresapproach |