Data Assimilation : The Ensemble Kalman Filter
Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filt...
Ausführliche Beschreibung
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| Format: |
E-Book |
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| Sprache: |
Englisch |
| Erschienen: |
Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg ; 2007 |
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| Systematik: |
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| Anmerkung: |
Includes bibliographical references (p. [267]-275) and index |
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| Umfang: |
Online-Ressource (XXI, 279 p. 63 illus, digital) |
| Weitere Ausgabe: |
Buchausg. u.d.T. Evensen, Geir: Data assimilation - Berlin : Springer, 2007 |
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| Reihe: |
SpringerLink ; Bücher |
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| Links: |
Link aufrufen |
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| ISBN: |
978-3-540-38301-7 |
| DOI / URN: |
10.1007/978-3-540-38301-7 |
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1645496376 |
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| 245 | 1 | 0 | |a Data Assimilation |b The Ensemble Kalman Filter |c by Geir Evensen |
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| 505 | 8 | 0 | |a Introduction; Statistical definitions; Analysis scheme; Sequential data assimilation; Variational inverse problems; Nonlinear variational inverse problems; Probabilistic formulation; Generalized Inverse; Ensemble methods; Statistical optimization; Sampling strategies for the EnKF; Model errors; Square Root Analysis schemes; Rank issues; An ocean prediction system; Estimation in an oil reservoir simulator |
| 520 | |a Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. It is demonstrated how the different methods can be derived from a common theoretical basis, as well as how they differ and/or are related to each other, and which properties characterize them, using several examples. Rather than emphasize a particular discipline such as oceanography or meteorology, it presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements. The mathematics level is modest, although it requires knowledge of basic spatial statistics, Bayesian statistics, and calculus of variations. Readers will also appreciate the introduction to the mathematical methods used and detailed derivations, which should be easy to follow, are given throughout the book. The codes used in several of the data assimilation experiments are available on a web page. In particular, this webpage contains a complete ensemble Kalman filter assimilation system, which forms an ideal starting point for a user who wants to implement the ensemble Kalman filter with his/her own dynamical model. The focus on ensemble methods, such as the ensemble Kalman filter and smoother, also makes it a solid reference to the derivation, implementation and application of such techniques. Much new material, in particular related to the formulation and solution of combined parameter and state estimation problems and the general properties of the ensemble algorithms, is available here for the first time. | ||
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9783540383017 978-3-540-38301-7 10.1007/978-3-540-38301-7 doi (DE-627)1645496376 (DE-576)264371976 (DE-599)BSZ264371976 (OCoLC)184984508 (OCoLC)315825357 (ZBM)1157.86001 (DE-He213)978-3-540-38301-7 (ZBM)1157.86001 DE-627 ger DE-627 rakwb eng XA-DE QA274.A1 RB bicssc SCI019000 bisacsh MAT000000 bisacsh SK 800 rvk (DE-625)rvk/143256: RB 10104 rvk (DE-625)rvk/142220:12617 *86-01 msc 86A32 msc 31.73 bkl 31.76 bkl Data Assimilation The Ensemble Kalman Filter by Geir Evensen Berlin, Heidelberg Springer-Verlag Berlin Heidelberg 2007 Online-Ressource (XXI, 279 p. 63 illus, digital) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier SpringerLink Bücher Includes bibliographical references (p. [267]-275) and index Introduction; Statistical definitions; Analysis scheme; Sequential data assimilation; Variational inverse problems; Nonlinear variational inverse problems; Probabilistic formulation; Generalized Inverse; Ensemble methods; Statistical optimization; Sampling strategies for the EnKF; Model errors; Square Root Analysis schemes; Rank issues; An ocean prediction system; Estimation in an oil reservoir simulator Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. It is demonstrated how the different methods can be derived from a common theoretical basis, as well as how they differ and/or are related to each other, and which properties characterize them, using several examples. Rather than emphasize a particular discipline such as oceanography or meteorology, it presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements. The mathematics level is modest, although it requires knowledge of basic spatial statistics, Bayesian statistics, and calculus of variations. Readers will also appreciate the introduction to the mathematical methods used and detailed derivations, which should be easy to follow, are given throughout the book. The codes used in several of the data assimilation experiments are available on a web page. In particular, this webpage contains a complete ensemble Kalman filter assimilation system, which forms an ideal starting point for a user who wants to implement the ensemble Kalman filter with his/her own dynamical model. The focus on ensemble methods, such as the ensemble Kalman filter and smoother, also makes it a solid reference to the derivation, implementation and application of such techniques. Much new material, in particular related to the formulation and solution of combined parameter and state estimation problems and the general properties of the ensemble algorithms, is available here for the first time. Distribution (Probability theory) Mathematical physics Engineering mathematics Geosciences Distribution (Probability theory) Mathematical physics Engineering mathematics s (DE-588)4803260-8 (DE-627)472314122 (DE-576)216547830 Datenassimilation gnd DE-101 s (DE-588)4803260-8 (DE-627)472314122 (DE-576)216547830 Datenassimilation gnd (DE-627) Evensen, Geir oth 9783540383000 Buchausg. u.d.T. Evensen, Geir Data assimilation Berlin : Springer, 2007 XXI, 279 S. 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9783540383017 978-3-540-38301-7 10.1007/978-3-540-38301-7 doi (DE-627)1645496376 (DE-576)264371976 (DE-599)BSZ264371976 (OCoLC)184984508 (OCoLC)315825357 (ZBM)1157.86001 (DE-He213)978-3-540-38301-7 (ZBM)1157.86001 DE-627 ger DE-627 rakwb eng XA-DE QA274.A1 RB bicssc SCI019000 bisacsh MAT000000 bisacsh SK 800 rvk (DE-625)rvk/143256: RB 10104 rvk (DE-625)rvk/142220:12617 *86-01 msc 86A32 msc 31.73 bkl 31.76 bkl Data Assimilation The Ensemble Kalman Filter by Geir Evensen Berlin, Heidelberg Springer-Verlag Berlin Heidelberg 2007 Online-Ressource (XXI, 279 p. 63 illus, digital) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier SpringerLink Bücher Includes bibliographical references (p. [267]-275) and index Introduction; Statistical definitions; Analysis scheme; Sequential data assimilation; Variational inverse problems; Nonlinear variational inverse problems; Probabilistic formulation; Generalized Inverse; Ensemble methods; Statistical optimization; Sampling strategies for the EnKF; Model errors; Square Root Analysis schemes; Rank issues; An ocean prediction system; Estimation in an oil reservoir simulator Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. It is demonstrated how the different methods can be derived from a common theoretical basis, as well as how they differ and/or are related to each other, and which properties characterize them, using several examples. Rather than emphasize a particular discipline such as oceanography or meteorology, it presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements. The mathematics level is modest, although it requires knowledge of basic spatial statistics, Bayesian statistics, and calculus of variations. Readers will also appreciate the introduction to the mathematical methods used and detailed derivations, which should be easy to follow, are given throughout the book. The codes used in several of the data assimilation experiments are available on a web page. In particular, this webpage contains a complete ensemble Kalman filter assimilation system, which forms an ideal starting point for a user who wants to implement the ensemble Kalman filter with his/her own dynamical model. The focus on ensemble methods, such as the ensemble Kalman filter and smoother, also makes it a solid reference to the derivation, implementation and application of such techniques. Much new material, in particular related to the formulation and solution of combined parameter and state estimation problems and the general properties of the ensemble algorithms, is available here for the first time. Distribution (Probability theory) Mathematical physics Engineering mathematics Geosciences Distribution (Probability theory) Mathematical physics Engineering mathematics s (DE-588)4803260-8 (DE-627)472314122 (DE-576)216547830 Datenassimilation gnd DE-101 s (DE-588)4803260-8 (DE-627)472314122 (DE-576)216547830 Datenassimilation gnd (DE-627) Evensen, Geir oth 9783540383000 Buchausg. u.d.T. Evensen, Geir Data assimilation Berlin : Springer, 2007 XXI, 279 S. (DE-627)516148826 (DE-576)260810045 9783540383000 354038300X https://doi.org/10.1007/978-3-540-38301-7 Verlag Volltext http://dx.doi.org/10.1007/978-3-540-38301-7 Resolving-System lizenzpflichtig Volltext https://swbplus.bsz-bw.de/bsz264371976cov.jpg V:DE-576 X:springer image/jpeg 20130402155035 Cover https://zbmath.org/?q=an:1157.86001 B:ZBM 2021-04-12 Verlag Zentralblatt MATH Inhaltstext http://www.gbv.de/dms/bowker/toc/9783540383000.pdf V:DE-601 X:Bowker pdf/application 2011-12-23 Verlag Inhaltsverzeichnis Inhaltsverzeichnis (DE-627)524975752 ZDB-2-EES 2007 GBV_ILN_23 ISIL_DE-830 SYSFLAG_1 GBV_KXP SSG-OPC-MAT GBV_ILN_60 ISIL_DE-705 GBV_ILN_65 ISIL_DE-3 GBV_ILN_120 ISIL_DE-715 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2027 ISIL_DE-105 GBV_ILN_2148 ISIL_DE-950 GBV ExPruef SK 800 Wahrscheinlichkeitstheorie Mathematik Monografien Wahrscheinlichkeitstheorie (DE-627)127092723X (DE-625)rvk/143256: (DE-576)20092723X RB 10104 Datenverarbeitung, Geoinformatik Geografie Nicht regional gebundene Darstellungen Allgemeine Geografie Hilfswissenschaften Datenverarbeitung, Geoinformatik (DE-627)127212973X (DE-625)rvk/142220:12617 (DE-576)20212973X 31.73 Mathematische Statistik SEPA (DE-627)106418998 31.76 Numerische Mathematik SEPA (DE-627)106408194 BO 045F 519.23 045F 519.2 045F 629.8/312 23 01 0830 814912710 00 --%%-- --%%-- s --%%-- olr-springer i z 30-03-07 60 01 0705 4304948644 SpringerLink Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Nur für Angehörige der HSU: Volltextzugang von außerhalb des Campus mit Anmeldung über Shibboleth mit Ihrer Bibliothekskennung z 07-04-23 65 01 0003 165565487X 03 --%%-- ebook --%%-- --%%-- OLR-SEB-ZDB-2-EES Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. k3o 02-01-17 120 01 0715 1840104309 OLR-ESP Campusweiter Zugriff (Universität Oldenburg). - Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden. z 13-01-19 120 02 0715 3582137112 00 --%%-- --%%-- g --%%-- alma z 24-01-20 370 01 4370 1845234960 olr-springer Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 27-01-19 2027 01 DE-105 3314444923 00 --%%-- --%%-- n --%%-- Campuslizenz l01 01-03-10 2148 01 DE-950 3314444982 00 --%%-- eBook Springer --%%-- n Elektronischer Volltext - Campuslizenz l01 03-05-07 23 01 0830 Springer EBook https://doi.org/10.1007/978-3-540-38301-7 60 01 0705 Volltextzugang Campus https://doi.org/10.1007/978-3-540-38301-7 65 01 0003 Volltextzugang Campus https://doi.org/10.1007/978-3-540-38301-7 120 01 0715 http://dx.doi.org/10.1007/978-3-540-38301-7 120 02 0715 http://49gbv-uob-primo.hosted.exlibrisgroup.com/openurl/49GBV_UOB/UOB_services_page?u.ignore_date_coverage=true&rft.mms_id=991016139049003501 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://doi.org/10.1007/978-3-540-38301-7 2148 01 DE-950 http://dx.doi.org/10.1007/978-3-540-38301-7 2027 01 DE-105 00 s ebook 120 00 DE-715 99 ww 23 01 0830 2006-06993, 2006-06996, 2006-06999, 2006-07000, 2006-07001, 2006-07003, 2006-07004, 2006-07005 120 01 0715 YH 2020 23 01 0830 olr-springer 60 01 0705 SpringerLink 65 01 0003 OLR-SEB-ZDB-2-EES 120 01 0715 OLR-ESP 120 02 0715 alma 370 01 4370 olr-springer 23 01 0830 2007.03.30 |
| allfields_unstemmed |
9783540383017 978-3-540-38301-7 10.1007/978-3-540-38301-7 doi (DE-627)1645496376 (DE-576)264371976 (DE-599)BSZ264371976 (OCoLC)184984508 (OCoLC)315825357 (ZBM)1157.86001 (DE-He213)978-3-540-38301-7 (ZBM)1157.86001 DE-627 ger DE-627 rakwb eng XA-DE QA274.A1 RB bicssc SCI019000 bisacsh MAT000000 bisacsh SK 800 rvk (DE-625)rvk/143256: RB 10104 rvk (DE-625)rvk/142220:12617 *86-01 msc 86A32 msc 31.73 bkl 31.76 bkl Data Assimilation The Ensemble Kalman Filter by Geir Evensen Berlin, Heidelberg Springer-Verlag Berlin Heidelberg 2007 Online-Ressource (XXI, 279 p. 63 illus, digital) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier SpringerLink Bücher Includes bibliographical references (p. [267]-275) and index Introduction; Statistical definitions; Analysis scheme; Sequential data assimilation; Variational inverse problems; Nonlinear variational inverse problems; Probabilistic formulation; Generalized Inverse; Ensemble methods; Statistical optimization; Sampling strategies for the EnKF; Model errors; Square Root Analysis schemes; Rank issues; An ocean prediction system; Estimation in an oil reservoir simulator Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. It is demonstrated how the different methods can be derived from a common theoretical basis, as well as how they differ and/or are related to each other, and which properties characterize them, using several examples. Rather than emphasize a particular discipline such as oceanography or meteorology, it presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements. The mathematics level is modest, although it requires knowledge of basic spatial statistics, Bayesian statistics, and calculus of variations. Readers will also appreciate the introduction to the mathematical methods used and detailed derivations, which should be easy to follow, are given throughout the book. The codes used in several of the data assimilation experiments are available on a web page. In particular, this webpage contains a complete ensemble Kalman filter assimilation system, which forms an ideal starting point for a user who wants to implement the ensemble Kalman filter with his/her own dynamical model. The focus on ensemble methods, such as the ensemble Kalman filter and smoother, also makes it a solid reference to the derivation, implementation and application of such techniques. Much new material, in particular related to the formulation and solution of combined parameter and state estimation problems and the general properties of the ensemble algorithms, is available here for the first time. Distribution (Probability theory) Mathematical physics Engineering mathematics Geosciences Distribution (Probability theory) Mathematical physics Engineering mathematics s (DE-588)4803260-8 (DE-627)472314122 (DE-576)216547830 Datenassimilation gnd DE-101 s (DE-588)4803260-8 (DE-627)472314122 (DE-576)216547830 Datenassimilation gnd (DE-627) Evensen, Geir oth 9783540383000 Buchausg. u.d.T. Evensen, Geir Data assimilation Berlin : Springer, 2007 XXI, 279 S. (DE-627)516148826 (DE-576)260810045 9783540383000 354038300X https://doi.org/10.1007/978-3-540-38301-7 Verlag Volltext http://dx.doi.org/10.1007/978-3-540-38301-7 Resolving-System lizenzpflichtig Volltext https://swbplus.bsz-bw.de/bsz264371976cov.jpg V:DE-576 X:springer image/jpeg 20130402155035 Cover https://zbmath.org/?q=an:1157.86001 B:ZBM 2021-04-12 Verlag Zentralblatt MATH Inhaltstext http://www.gbv.de/dms/bowker/toc/9783540383000.pdf V:DE-601 X:Bowker pdf/application 2011-12-23 Verlag Inhaltsverzeichnis Inhaltsverzeichnis (DE-627)524975752 ZDB-2-EES 2007 GBV_ILN_23 ISIL_DE-830 SYSFLAG_1 GBV_KXP SSG-OPC-MAT GBV_ILN_60 ISIL_DE-705 GBV_ILN_65 ISIL_DE-3 GBV_ILN_120 ISIL_DE-715 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2027 ISIL_DE-105 GBV_ILN_2148 ISIL_DE-950 GBV ExPruef SK 800 Wahrscheinlichkeitstheorie Mathematik Monografien Wahrscheinlichkeitstheorie (DE-627)127092723X (DE-625)rvk/143256: (DE-576)20092723X RB 10104 Datenverarbeitung, Geoinformatik Geografie Nicht regional gebundene Darstellungen Allgemeine Geografie Hilfswissenschaften Datenverarbeitung, Geoinformatik (DE-627)127212973X (DE-625)rvk/142220:12617 (DE-576)20212973X 31.73 Mathematische Statistik SEPA (DE-627)106418998 31.76 Numerische Mathematik SEPA (DE-627)106408194 BO 045F 519.23 045F 519.2 045F 629.8/312 23 01 0830 814912710 00 --%%-- --%%-- s --%%-- olr-springer i z 30-03-07 60 01 0705 4304948644 SpringerLink Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Nur für Angehörige der HSU: Volltextzugang von außerhalb des Campus mit Anmeldung über Shibboleth mit Ihrer Bibliothekskennung z 07-04-23 65 01 0003 165565487X 03 --%%-- ebook --%%-- --%%-- OLR-SEB-ZDB-2-EES Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. k3o 02-01-17 120 01 0715 1840104309 OLR-ESP Campusweiter Zugriff (Universität Oldenburg). - Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden. z 13-01-19 120 02 0715 3582137112 00 --%%-- --%%-- g --%%-- alma z 24-01-20 370 01 4370 1845234960 olr-springer Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 27-01-19 2027 01 DE-105 3314444923 00 --%%-- --%%-- n --%%-- Campuslizenz l01 01-03-10 2148 01 DE-950 3314444982 00 --%%-- eBook Springer --%%-- n Elektronischer Volltext - Campuslizenz l01 03-05-07 23 01 0830 Springer EBook https://doi.org/10.1007/978-3-540-38301-7 60 01 0705 Volltextzugang Campus https://doi.org/10.1007/978-3-540-38301-7 65 01 0003 Volltextzugang Campus https://doi.org/10.1007/978-3-540-38301-7 120 01 0715 http://dx.doi.org/10.1007/978-3-540-38301-7 120 02 0715 http://49gbv-uob-primo.hosted.exlibrisgroup.com/openurl/49GBV_UOB/UOB_services_page?u.ignore_date_coverage=true&rft.mms_id=991016139049003501 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://doi.org/10.1007/978-3-540-38301-7 2148 01 DE-950 http://dx.doi.org/10.1007/978-3-540-38301-7 2027 01 DE-105 00 s ebook 120 00 DE-715 99 ww 23 01 0830 2006-06993, 2006-06996, 2006-06999, 2006-07000, 2006-07001, 2006-07003, 2006-07004, 2006-07005 120 01 0715 YH 2020 23 01 0830 olr-springer 60 01 0705 SpringerLink 65 01 0003 OLR-SEB-ZDB-2-EES 120 01 0715 OLR-ESP 120 02 0715 alma 370 01 4370 olr-springer 23 01 0830 2007.03.30 |
| allfieldsGer |
9783540383017 978-3-540-38301-7 10.1007/978-3-540-38301-7 doi (DE-627)1645496376 (DE-576)264371976 (DE-599)BSZ264371976 (OCoLC)184984508 (OCoLC)315825357 (ZBM)1157.86001 (DE-He213)978-3-540-38301-7 (ZBM)1157.86001 DE-627 ger DE-627 rakwb eng XA-DE QA274.A1 RB bicssc SCI019000 bisacsh MAT000000 bisacsh SK 800 rvk (DE-625)rvk/143256: RB 10104 rvk (DE-625)rvk/142220:12617 *86-01 msc 86A32 msc 31.73 bkl 31.76 bkl Data Assimilation The Ensemble Kalman Filter by Geir Evensen Berlin, Heidelberg Springer-Verlag Berlin Heidelberg 2007 Online-Ressource (XXI, 279 p. 63 illus, digital) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier SpringerLink Bücher Includes bibliographical references (p. [267]-275) and index Introduction; Statistical definitions; Analysis scheme; Sequential data assimilation; Variational inverse problems; Nonlinear variational inverse problems; Probabilistic formulation; Generalized Inverse; Ensemble methods; Statistical optimization; Sampling strategies for the EnKF; Model errors; Square Root Analysis schemes; Rank issues; An ocean prediction system; Estimation in an oil reservoir simulator Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. It is demonstrated how the different methods can be derived from a common theoretical basis, as well as how they differ and/or are related to each other, and which properties characterize them, using several examples. Rather than emphasize a particular discipline such as oceanography or meteorology, it presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements. The mathematics level is modest, although it requires knowledge of basic spatial statistics, Bayesian statistics, and calculus of variations. Readers will also appreciate the introduction to the mathematical methods used and detailed derivations, which should be easy to follow, are given throughout the book. The codes used in several of the data assimilation experiments are available on a web page. In particular, this webpage contains a complete ensemble Kalman filter assimilation system, which forms an ideal starting point for a user who wants to implement the ensemble Kalman filter with his/her own dynamical model. The focus on ensemble methods, such as the ensemble Kalman filter and smoother, also makes it a solid reference to the derivation, implementation and application of such techniques. Much new material, in particular related to the formulation and solution of combined parameter and state estimation problems and the general properties of the ensemble algorithms, is available here for the first time. Distribution (Probability theory) Mathematical physics Engineering mathematics Geosciences Distribution (Probability theory) Mathematical physics Engineering mathematics s (DE-588)4803260-8 (DE-627)472314122 (DE-576)216547830 Datenassimilation gnd DE-101 s (DE-588)4803260-8 (DE-627)472314122 (DE-576)216547830 Datenassimilation gnd (DE-627) Evensen, Geir oth 9783540383000 Buchausg. u.d.T. Evensen, Geir Data assimilation Berlin : Springer, 2007 XXI, 279 S. (DE-627)516148826 (DE-576)260810045 9783540383000 354038300X https://doi.org/10.1007/978-3-540-38301-7 Verlag Volltext http://dx.doi.org/10.1007/978-3-540-38301-7 Resolving-System lizenzpflichtig Volltext https://swbplus.bsz-bw.de/bsz264371976cov.jpg V:DE-576 X:springer image/jpeg 20130402155035 Cover https://zbmath.org/?q=an:1157.86001 B:ZBM 2021-04-12 Verlag Zentralblatt MATH Inhaltstext http://www.gbv.de/dms/bowker/toc/9783540383000.pdf V:DE-601 X:Bowker pdf/application 2011-12-23 Verlag Inhaltsverzeichnis Inhaltsverzeichnis (DE-627)524975752 ZDB-2-EES 2007 GBV_ILN_23 ISIL_DE-830 SYSFLAG_1 GBV_KXP SSG-OPC-MAT GBV_ILN_60 ISIL_DE-705 GBV_ILN_65 ISIL_DE-3 GBV_ILN_120 ISIL_DE-715 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2027 ISIL_DE-105 GBV_ILN_2148 ISIL_DE-950 GBV ExPruef SK 800 Wahrscheinlichkeitstheorie Mathematik Monografien Wahrscheinlichkeitstheorie (DE-627)127092723X (DE-625)rvk/143256: (DE-576)20092723X RB 10104 Datenverarbeitung, Geoinformatik Geografie Nicht regional gebundene Darstellungen Allgemeine Geografie Hilfswissenschaften Datenverarbeitung, Geoinformatik (DE-627)127212973X (DE-625)rvk/142220:12617 (DE-576)20212973X 31.73 Mathematische Statistik SEPA (DE-627)106418998 31.76 Numerische Mathematik SEPA (DE-627)106408194 BO 045F 519.23 045F 519.2 045F 629.8/312 23 01 0830 814912710 00 --%%-- --%%-- s --%%-- olr-springer i z 30-03-07 60 01 0705 4304948644 SpringerLink Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Nur für Angehörige der HSU: Volltextzugang von außerhalb des Campus mit Anmeldung über Shibboleth mit Ihrer Bibliothekskennung z 07-04-23 65 01 0003 165565487X 03 --%%-- ebook --%%-- --%%-- OLR-SEB-ZDB-2-EES Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. k3o 02-01-17 120 01 0715 1840104309 OLR-ESP Campusweiter Zugriff (Universität Oldenburg). - Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden. z 13-01-19 120 02 0715 3582137112 00 --%%-- --%%-- g --%%-- alma z 24-01-20 370 01 4370 1845234960 olr-springer Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 27-01-19 2027 01 DE-105 3314444923 00 --%%-- --%%-- n --%%-- Campuslizenz l01 01-03-10 2148 01 DE-950 3314444982 00 --%%-- eBook Springer --%%-- n Elektronischer Volltext - Campuslizenz l01 03-05-07 23 01 0830 Springer EBook https://doi.org/10.1007/978-3-540-38301-7 60 01 0705 Volltextzugang Campus https://doi.org/10.1007/978-3-540-38301-7 65 01 0003 Volltextzugang Campus https://doi.org/10.1007/978-3-540-38301-7 120 01 0715 http://dx.doi.org/10.1007/978-3-540-38301-7 120 02 0715 http://49gbv-uob-primo.hosted.exlibrisgroup.com/openurl/49GBV_UOB/UOB_services_page?u.ignore_date_coverage=true&rft.mms_id=991016139049003501 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://doi.org/10.1007/978-3-540-38301-7 2148 01 DE-950 http://dx.doi.org/10.1007/978-3-540-38301-7 2027 01 DE-105 00 s ebook 120 00 DE-715 99 ww 23 01 0830 2006-06993, 2006-06996, 2006-06999, 2006-07000, 2006-07001, 2006-07003, 2006-07004, 2006-07005 120 01 0715 YH 2020 23 01 0830 olr-springer 60 01 0705 SpringerLink 65 01 0003 OLR-SEB-ZDB-2-EES 120 01 0715 OLR-ESP 120 02 0715 alma 370 01 4370 olr-springer 23 01 0830 2007.03.30 |
| allfieldsSound |
9783540383017 978-3-540-38301-7 10.1007/978-3-540-38301-7 doi (DE-627)1645496376 (DE-576)264371976 (DE-599)BSZ264371976 (OCoLC)184984508 (OCoLC)315825357 (ZBM)1157.86001 (DE-He213)978-3-540-38301-7 (ZBM)1157.86001 DE-627 ger DE-627 rakwb eng XA-DE QA274.A1 RB bicssc SCI019000 bisacsh MAT000000 bisacsh SK 800 rvk (DE-625)rvk/143256: RB 10104 rvk (DE-625)rvk/142220:12617 *86-01 msc 86A32 msc 31.73 bkl 31.76 bkl Data Assimilation The Ensemble Kalman Filter by Geir Evensen Berlin, Heidelberg Springer-Verlag Berlin Heidelberg 2007 Online-Ressource (XXI, 279 p. 63 illus, digital) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier SpringerLink Bücher Includes bibliographical references (p. [267]-275) and index Introduction; Statistical definitions; Analysis scheme; Sequential data assimilation; Variational inverse problems; Nonlinear variational inverse problems; Probabilistic formulation; Generalized Inverse; Ensemble methods; Statistical optimization; Sampling strategies for the EnKF; Model errors; Square Root Analysis schemes; Rank issues; An ocean prediction system; Estimation in an oil reservoir simulator Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. It is demonstrated how the different methods can be derived from a common theoretical basis, as well as how they differ and/or are related to each other, and which properties characterize them, using several examples. Rather than emphasize a particular discipline such as oceanography or meteorology, it presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements. The mathematics level is modest, although it requires knowledge of basic spatial statistics, Bayesian statistics, and calculus of variations. Readers will also appreciate the introduction to the mathematical methods used and detailed derivations, which should be easy to follow, are given throughout the book. The codes used in several of the data assimilation experiments are available on a web page. In particular, this webpage contains a complete ensemble Kalman filter assimilation system, which forms an ideal starting point for a user who wants to implement the ensemble Kalman filter with his/her own dynamical model. The focus on ensemble methods, such as the ensemble Kalman filter and smoother, also makes it a solid reference to the derivation, implementation and application of such techniques. Much new material, in particular related to the formulation and solution of combined parameter and state estimation problems and the general properties of the ensemble algorithms, is available here for the first time. Distribution (Probability theory) Mathematical physics Engineering mathematics Geosciences Distribution (Probability theory) Mathematical physics Engineering mathematics s (DE-588)4803260-8 (DE-627)472314122 (DE-576)216547830 Datenassimilation gnd DE-101 s (DE-588)4803260-8 (DE-627)472314122 (DE-576)216547830 Datenassimilation gnd (DE-627) Evensen, Geir oth 9783540383000 Buchausg. u.d.T. Evensen, Geir Data assimilation Berlin : Springer, 2007 XXI, 279 S. 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[267]-275) and index</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="a">Introduction; Statistical definitions; Analysis scheme; Sequential data assimilation; Variational inverse problems; Nonlinear variational inverse problems; Probabilistic formulation; Generalized Inverse; Ensemble methods; Statistical optimization; Sampling strategies for the EnKF; Model errors; Square Root Analysis schemes; Rank issues; An ocean prediction system; Estimation in an oil reservoir simulator</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. 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In particular, this webpage contains a complete ensemble Kalman filter assimilation system, which forms an ideal starting point for a user who wants to implement the ensemble Kalman filter with his/her own dynamical model. The focus on ensemble methods, such as the ensemble Kalman filter and smoother, also makes it a solid reference to the derivation, implementation and application of such techniques. 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List of symbols p. XV Introduction … Statistical definitions … Probability density function … Statistical moments … Expected value … Variance … Covariance … Working with samples from a distribution … Sample mean … Sample variance … Sample covariance … Statistics of random fields … Sample mean … Sample variance … Sample covariance … Correlation … Bias … Central limit theorem … Analysis scheme … Scalar case … State-space formulation … Bayesian formulation … Extension to spatial dimensions … Basic formulation … Euler-Lagrange equation … Representer solution … Representer matrix … Error estimate … Uniqueness of the solution … Minimization of the penalty function … Prior and posterior value of the penalty function … Discrete form … Sequential data assimilation … Linear Dynamics … Kalman filter for a scalar case … Kalman filter for a vector state … Kalman filter with a linear advection equation … Nonlinear dynamics … Extended Kalman filter for the scalar case … Extended Kalman filter in matrix form … Example using the extended Kalman filter … Extended Kalman filter for the mean … Discussion … Ensemble Kalman filter … Representation of error statistics … Prediction of error statistics … Analysis scheme … Discussion … Example with a QG model … Variational inverse problems … Simple illustration … Linear inverse problem … Model and observations … Measurement functional … Comment on the measurement equation … Statistical hypothesis … Weak constraint variational formulation … Extremum of the penalty function … Euler-Lagrange equations … Strong constraint approximation … Solution by representer expansions … Representer method with an Ekman model … Inverse problem … Variational formulation … Euler-Lagrange equations … Representer solution … Example experiment … Assimilation of real measurements … Comments on the representer method … Nonlinear variational inverse problems … Extension to nonlinear dynamics … Generalized inverse for the Lorenz equations … Strong constraint assumption … Solution of the weak constraint problem … Minimization by the gradient descent method … Minimization by genetic algorithms … Example with the Lorenz equations … Estimating the model error covariance … Time correlation of the model error covariance … Inversion experiments … Discussion … Probabilistic formulation … Joint parameter and state estimation … Model equations and measurements … Bayesian formulation … Discrete formulation … Sequential processing of measurements … Summary … Generalized Inverse … Generalized inverse formulation … Prior density for the poorly known parameters … Prior density for the initial conditions … Prior density for the boundary conditions … Prior density for the measurements … Prior density for the model errors … Conditional joint density … Solution methods for the generalized inverse problem … Generalized inverse for a scalar model … Euler-Lagrange equations … Iteration in [alpha] … Strong constraint problem … Parameter estimation in the Ekman flow model … Summary … Ensemble methods … Introductory remarks … Linear ensemble analysis update … Ensemble representation of error statistics … Ensemble representation for measurements … Ensemble Smoother (ES) … Ensemble Kalman Smoother (EnKS) … Ensemble Kalman Filter (EnKF) … EnKF with linear noise free model … EnKS using EnKF as a prior … Example with the Lorenz equations … Description of experiments … Assimilation Experiment … Discussion … Statistical optimization … Definition of the minimization problem … Parameters … Model … Measurements … Cost function … Bayesian formalism … Solution by ensemble methods … Variance minimizing solution … EnKS solution … Examples … Discussion … Sampling strategies for the EnKF … Introduction … Simulation of realizations … Inverse Fourier transform … Definition of Fourier spectrum … Specification of covariance and variance … Simulating correlated fields … Improved sampling scheme … Experiments … Overview of experiments … Impact from ensemble size … Impact of improved sampling for the initial ensemble … Improved sampling of measurement perturbations … Evolution of ensemble singular spectra … Summary … Model errors … Simulation of model errors … Determination of [rho] … Physical model … Variance growth due to the stochastic forcing … Updating model noise using measurements … Scalar model … Variational inverse problem … Prior statistics … Penalty function … Euler-Lagrange equations … Iteration of parameter … Solution by representer expansions … Variance growth due to model errors … Formulation as a stochastic model … Examples … Case A0 … Case A1 … Case B … Case C … Discussion … Square Root Analysis schemes … Square root algorithm for the EnKF analysis … Updating the ensemble mean … Updating the ensemble perturbations … Randomization of the analysis update … Final update equation in the square root algorithms … Experiments … Overview of experiments … Impact of the square root analysis algorithm … Rank issues … Pseudo inverse of C … Pseudo inverse … Interpretation … Analysis schemes using the pseudo inverse of C … Example … Efficient subspace pseudo inversion … Derivation of the subspace pseudo inverse … Analysis schemes based on the subspace pseudo inverse … An interpretation of the subspace pseudo inversion … Subspace inversion using a low-rank C[subscript isin isin] … Derivation of the pseudo inverse … Analysis schemes using a low-rank C[subscript isin isin] … Implementation of the analysis schemes … Rank issues related to the use of a low-rank C[subscript isin isin] … Experiments with m [dested greater greater] N … Summary … An ocean prediction system … Introduction … System configuration and EnKF implementation … Nested regional models … Summary … Estimation in an oil reservoir simulator … Introduction … Experiment … Parameterization … State vector … Results … Summary … Other EnKF issues … Local analysis … Nonlinear measurements in the EnKF … Assimilation of non-synoptic measurements … Time difference data … Ensemble Optimal Interpolation (EnOI) … Chronology of ensemble assimilation developments … Applications of the EnKF … Other ensemble based filters … Ensemble smoothers … Ensemble methods for parameter estimation … Nonlinear filters and smoothers … References … Index … |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000cam a22002652 4500</leader><controlfield tag="001">1645496376</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230428111317.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">070503s2007 gw |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9783540383017</subfield><subfield code="9">978-3-540-38301-7</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/978-3-540-38301-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1645496376</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-576)264371976</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BSZ264371976</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)184984508</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)315825357</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZBM)1157.86001</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)978-3-540-38301-7</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZBM)1157.86001</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="c">XA-DE</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA274.A1</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">RB</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">SCI019000</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">MAT000000</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SK 800</subfield><subfield code="2">rvk</subfield><subfield code="0">(DE-625)rvk/143256:</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">RB 10104</subfield><subfield code="2">rvk</subfield><subfield code="0">(DE-625)rvk/142220:12617</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">*86-01</subfield><subfield code="2">msc</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">86A32</subfield><subfield code="2">msc</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.73</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.76</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Data Assimilation</subfield><subfield code="b">The Ensemble Kalman Filter</subfield><subfield code="c">by Geir Evensen</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Berlin, Heidelberg</subfield><subfield code="b">Springer-Verlag Berlin Heidelberg</subfield><subfield code="c">2007</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">Online-Ressource (XXI, 279 p. 63 illus, digital)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">SpringerLink</subfield><subfield code="a">Bücher</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references (p. [267]-275) and index</subfield></datafield><datafield tag="505" ind1="8" ind2="0"><subfield code="a">Introduction; Statistical definitions; Analysis scheme; Sequential data assimilation; Variational inverse problems; Nonlinear variational inverse problems; Probabilistic formulation; Generalized Inverse; Ensemble methods; Statistical optimization; Sampling strategies for the EnKF; Model errors; Square Root Analysis schemes; Rank issues; An ocean prediction system; Estimation in an oil reservoir simulator</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. It is demonstrated how the different methods can be derived from a common theoretical basis, as well as how they differ and/or are related to each other, and which properties characterize them, using several examples. Rather than emphasize a particular discipline such as oceanography or meteorology, it presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements. The mathematics level is modest, although it requires knowledge of basic spatial statistics, Bayesian statistics, and calculus of variations. Readers will also appreciate the introduction to the mathematical methods used and detailed derivations, which should be easy to follow, are given throughout the book. The codes used in several of the data assimilation experiments are available on a web page. 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