Statistical learning to model stratospheric variability:
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
2012
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| Schlagworte: | |
| Online-Zugang: | Volltext Inhaltsverzeichnis |
| Beschreibung: | Nebentitel: Statistisches Lernen zur Modellierung von stratosphärischer Variabilität |
| Beschreibung: | III, 145, 2 S. Ill., graph. Darst., Kt. |
Internformat
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| 245 | 1 | 0 | |a Statistical learning to model stratospheric variability |c by Christian Blume |
| 246 | 1 | 3 | |a Statistisches Lernen zur Modellierung von stratosphärischer Variabilität |
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Datensatz im Suchindex
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|---|---|
| adam_text | IMAGE 1
CONTENTS
1 INTRODUCTION 1
2 STRATOSPHERIC VARIABILITY 5
2.1 S U D D E N S T R A T O S P H E R I C W A R M I N G S 6
2 . 2 N A T U R A L VARIABILITY F A C T O R S 9
2.2.1 T H E QUASI-BIENNIAL OSCILLATION 9
2 . 2 . 2 T H E A N N U L A R M O D E S 1 1
2 . 2 . 3 S E A S U R F A C E T E M P E R A T U R E S 1 3
2 . 2 . 4 T H E 11-YEAR SOLAR CYCLE 1 5
2 . 2 . 5 VOLCANIC E R U P T I O N S 1 7
2 . 3 T E M P E R A T U R E A N D O Z O N E T R E N D S 1 8
2.3.1 R E C E N T C L I M A T E 1 8
2.3.2 F U T U R E P R O J E C T I O N S 2 0
2 . 4 STATISTICAL ANALYSIS 2 1
3 STATISTICAL LEARNING APPROACHES 2 5
3.1 PRINCIPAL C O M P O N E N T ANALYSIS ( P C A ) 2 6
3.2 LINEAR D I S C R I M I N A N T ANALYSIS (LDA) 2 9
3.3 FINITE E L E M E N T M E T H O D PLUS V E C T O R A U T O - R E G R
E S S I O N ( F E M - V A R X ) 3 2
3 . 4 MULTI-LAYER P E R C E P T R O N ( M L P ) 3 5
3.5 S U P P O R T V E C T O R M A C H I N E S ( S V M S ) 3 8
3.5.1 S U P P O R T V E C T O R CLASSIFICATION 3 8
3.5.2 S U P P O R T V E C T O R REGRESSION 4 1
3.6 P E R F O R M A N C E M E A S U R E S 4 2
3.6.1 CLASSIFICATION 4 2
3.6.2 REGRESSION 4 3
3.7 O P T I M A L MODEL A R C H I T E C T U R E 4 4
3.7.1 INFORMATION CRITERIA 4 4
3.7.2 CROSS-VALIDATION 4 6
3.7.3 MODEL A V E R A G I N G 4 6
3.8 B O O T S T R A P P I N G 4 7
HTTP://D-NB.INFO/1026498163
IMAGE 2
3.8.1 C A S E RESAMPLING 4 7
3.8.2 GAUSSIAN P R O C E S S S A M P L I N G 4 8
3 . 8 . 3 CONFIDENCE INTERVAL 4 8
3 . 9 NORMALIZATION A N D I M P A C T O F T H E F E A T U R E S 4 9
3.9.1 NORMALIZATION 4 9
3 . 9 . 2 I M P A C T 4 9
3 . 1 0 C O M P A R I N G T H E LEARNING A P P R O A C H E S 5 1
4 D A T A 5 2
4.1 O B S E R V A T I O N S 5 2
4 . 2 REANALYSES 5 3
4 . 3 C H E M I S T R Y - C L I M A T E MODELS 5 4
5 QUANTIFICATION O F NATURAL VARIABILITY 5 7
5.1 DIMENSION R E D U C T I O N 5 8
5 . 2 O P T I M A L MODEL A R C H I T E C T U R E 6 1
5 . 3 REGRESSION P E R F O R M A N C E 6 4
5 . 4 E S T I M A T I N G T H E STATISTICAL I M P O R T A N C E 6 7
5.4.1 INDIVIDUAL I M P A C T S 6 8
5.4.2 AVERAGED I M P A C T S A N D N A T U R A L VARIABILITY 7 2
5 . 4 . 3 REGIONAL I M P O R T A N C E 7 5
5 . 5 LONG-TERM FORECASTING 7 8
5 . 6 CONCLUDING R E M A R K S 8 1
6 CLASSIFICATION O F SUDDEN STRATOSPHERIC WARMINGS 8 5
6.1 D A T A A N D EXTERNAL F A C T O R S 8 7
6 . 2 T E M P E R A T U R E R E P R E S E N T A T I O N 8 8
6 . 3 TRAINING S A M P L E 8 9
6 . 4 M E M O R Y IN T H E S Y S T E M 9 1
6 . 5 STATISTICAL M E T H O D S FOR CLASSIFICATION 9 1
6 . 6 O P T I M A L MODEL A R C H I T E C T U R E 9 2
6 . 7 PROBABILITIES O F S T R A T O S P H E R I C W A R M I N G S 9 4
6.7.1 T H R E E S A M P L E W I N T E R S 9 4
6 . 7 . 2 CLASSIFICATION P E R F O R M A N C E 9 5
6 . 7 . 3 I M P A C T O F T H E I N P U T N E U R O N S 9 6
6 . 8 S T R A T O S P H E R I C W A R M I N G CLIMATOLOGIES 9 7
6.8.1 W A R M I N G E V E N T S 9 7
6 . 8 . 2 C H A N G E IN CIRCULATION 9 8
6 . 8 . 3 S T R A T O S P H E R I C W A R M I N G FREQUENCIES 9 9
6 . 8 . 4 MARGINALIZED PROBABILITY DISTRIBUTIONS 1 0 1
IMAGE 3
6 . 9 CLASSIFICATION IN C C M S I M U L A T I O N S 1 0 4
6.9.1 R E F - B 1 SIMULATIONS: EMAC, MRI, W A C C M 1 0 5
6 . 9 . 2 A S T U D Y O F SENSITIVITY W I T H W A C C M 1 0 7
6 . 1 0 C O N C L U D I N G R E M A R K S 1 1 0
7 FORECASTING POLAR STRATOSPHERIC VARIABILITY 1 1 4
7.1 D A T A A N D EXTERNAL F A C T O R S 1 1 5
7 . 2 O P T I M A L MODEL A R C H I T E C T U R E 1 1 7
7 . 3 TRAINING A N D H I N D C A S T PERIOD 1 1 8
7 . 4 MODEL SENSITIVITY 119
7.5 I M P A C T O F EXTERNAL F A C T O R S 1 2 0
7.6 T H E W I N T E R 2 0 1 1 / 1 2 - F O R E C A S T 122
7.7 T H E W I N T E R 2 0 1 1 / 1 2 - O B S E R V A T I O N S 1 2 3
7 . 8 CONCLUDING R E M A R K S 1 2 4
8 CONCLUSIONS AND OUTLOOK 1 2 7
BIBLIOGRAPHY 1 3 4
ABBREVIATIONS 1 4 3
ACKNOWLEDGMENTS 1 4 5
I I I
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| discipline | Geologie / Paläontologie |
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| spelling | Blume, Christian 1982- Verfasser (DE-588)102436786X aut Statistical learning to model stratospheric variability by Christian Blume Statistisches Lernen zur Modellierung von stratosphärischer Variabilität 2012 III, 145, 2 S. Ill., graph. Darst., Kt. txt rdacontent n rdamedia nc rdacarrier Nebentitel: Statistisches Lernen zur Modellierung von stratosphärischer Variabilität Berlin, Freie Univ., Diss., 2012 (DE-588)4113937-9 Hochschulschrift gnd-content Erscheint auch als Online-Ausgabe urn:nbn:de:kobv:188-fudissthesis000000038159-4 http://www.diss.fu-berlin.de/diss/receive/FUDISS_thesis_000000038159 kostenfrei Volltext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025175729&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Blume, Christian 1982- Statistical learning to model stratospheric variability |
| subject_GND | (DE-588)4113937-9 |
| title | Statistical learning to model stratospheric variability |
| title_alt | Statistisches Lernen zur Modellierung von stratosphärischer Variabilität |
| title_auth | Statistical learning to model stratospheric variability |
| title_exact_search | Statistical learning to model stratospheric variability |
| title_full | Statistical learning to model stratospheric variability by Christian Blume |
| title_fullStr | Statistical learning to model stratospheric variability by Christian Blume |
| title_full_unstemmed | Statistical learning to model stratospheric variability by Christian Blume |
| title_short | Statistical learning to model stratospheric variability |
| title_sort | statistical learning to model stratospheric variability |
| topic_facet | Hochschulschrift |
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