Advanced data assimilation for geosciences: École de Physique des Houches, special Issue, 28 May-15 June 2012
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| Format: | Tagungsbericht Buch |
| Sprache: | English |
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Oxford
Oxford University Press
2015
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| Ausgabe: | 1. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXIV, 584 S. Ill., graph. Darst. |
| ISBN: | 9780198723844 |
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| 245 | 1 | 0 | |a Advanced data assimilation for geosciences |b École de Physique des Houches, special Issue, 28 May-15 June 2012 |c ed. by Eric Blayo ... |
| 250 | |a 1. ed. | ||
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| 300 | |a XXIV, 584 S. |b Ill., graph. Darst. | ||
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CONTENTS
LIST
OF
PARTICIPANTS
PART
I
KEY
LECTURES
1
4D-VAR:
FOUR-DIMENSIONAL
VARIATIONAL
ASSIMILATION
O.
TALAGRAND
1.1
INTRODUCTION
1.2
VARIATIONAL
ASSIMILATION
IN
THE CONTEXT
OF
STATISTICAL
LINEAR ESTIMATION
1.3
MINIMIZATION
METHODS.
THE ADJOINT
APPROACH
1.4
PRACTICAL
IMPLEMENTATION
1.5
FURTHER
CONSIDERATIONS ON
VARIATIONAL
ASSIMILATION
1.6
MORE ON
THE ADJOINT
METHOD
1.7
CONCLUSIONS
RE[ERENCES
2
FOUR-DIMENSIONAL
VARIATIONAL
DATA
ASSIMILATION
A.
C.
LORENC
2.1
4D-VAR:
BACKGROUND AND
MOTIVATION
2.2
4D-VAR:
DERIVATION
2.3
4D-VAR:
ADVANCED
ASPECTS
2.4
4D-VAR: COUPLING
WITH
ENSEMBLES
REFERENCES
3
INTRODUCTION
TO
THE KALMAN
FILTER
C. SNYDER
3.1
A
BAYESIAN
VIEW
OF
DATA
ASSIMILATION
3.2
THE KALMAN
FILTER
(I)
3.3
A
CLOSER
LOOK
AT
THE
FORECAST
AND
UPDATE
STEPS
3.4
THE
KALMAN
FILTER
(II)
3.5
ASSORTED TOPICS
3.6
NONLINEARITY
AND NON-GAUSSIANITY
3.7
BASICS
OF
THE
ENSEMBLE
KALMAN
FILTER
3.8
ASSORTED DERIVATIONS AND IDENTITIES
R,EFERENCES
4
SMOOTHERS
E.
COSME
4.7
INTRODUCTION
4.2
SMOOTHING ALGORITHMS
IN
A
BAYESIAN
FRAMEWORK
4.3
LINEAR
GAUSSIAN
SMOOTHERS
3
5
5
T2
T7
19
23
25
26
31
J.)
47
52
63
68
TO
77
84
89
94
96
103
106
118
119
XLX
TZL
123
724
R27
XIV
CONTENTS
4.4
ENSEMBLE
SMOOTHERS
4.5
ADVANTAGES,
DRAWBACKS,
AND HIGH-DIMENSIONAL
APPLICATIONS
REFERENCES
5
OBSERVATION INFLUENCE
DIAGNOSTIC
OF
A
DATA
ASSIMILATION
SYSTEM
131
133
135
C.
CARDINALI
5.1
INTRODUCTION
5.2
CLASSICAL
STATISTICAL
DEFINITIONS
OF
INFLUENCE
MATRIX
AND
5.3
OBSERVATIONAL INFLUENCE
AND
SELF-SENSITIVITY
FOR
A
DA
SCHEME
5.4
RESULTS
5.5
CONCLUSIONS
ACKNOWLEDGEMENTS
137
139
SELF-SENSITIVITY
741
743
148
156
158
SCHEME
158
160
T62
165
767
168
173
T78
179
180
183
R.85
187
792
195
199
206
206
209
2TT
212
279
219
22T
226
227
APPENDIX
1:
INFLUENCE
MATRIX
CALCULATION
IN
WEIGHTED
REGRESSION
DA
APPENDIX
2:
APPROXIMATE
CALCULATION
OF
SELF-SENSITIVITY
IN
A LARGE
VARIATIONAL
ANALYSIS
SYSTEM
REFERENCES
OBSERVATION
IMPACT
ON
THE
SHORT-RANGE
FORECAST
C.
CARDINALI
6.1
INTRODUCTION
6.2
OBSERVATIONAL
IMPACT
ON
THE
FORECAST
6.3
RESULTS
6.4
CONCLUSION
ACKNOWLEDGEMENTS
REFERENCES
PART
LL
SPECIALIZED
LECTURES
BACKGROUND
ERROR
COVARIANCES:
ESTIMATION
AND SPECIFICATION
L.
BERRE
7.1
ERROR
EQUATIONS AND
THEIR
SIMULATION
7.2
INNOVATION-BASED
ESTIMATIONS
7.3
DIAGNOSIS
OF BACKGROUND
ERROR
COVARIANCES
7.4
MODELLING
AND
FILTERING
COVARIANCES
7.5
CONCLUSIONS
REFERENCES
OBSERVATION
ERROR
SPECIFICATIONS
G.
DESROZIERS
8.1
GENERAL FRAMEWORK
8.2
METHODS
FOR
ESTIMATING
OBSERVATION
ERROR
STATISTICS
8.3
DIAGNOSIS
OF
OBSERVATION
ERROR
VARIANCES
8.4
DIAGNOSIS
OF OBSERVATION
ERROR
CORRELATIONS
8.5
OBSERVATION
ERROR
CORRELATION
SPECIFICATION
IN THE
ASSIMILATION
8.6
CONCLUSION
REFERENCES
10
ERRORS.
A
POSTERIORI
DIAGNOSTICS
O.
TALAGR,AND
9.1
INTRODUCTION
9.2
REMINDER ON
STATISTICAL LINEAR
ESTIMATIORR
9.3
OBJECTIVE EVALUATION
OF
ASSIMILATION ALGORITHMS
9.4
ESTIMATION
OF
THE STATISTICS
OF DATA
ERRORS
9.5
DIAGNOSTICS
OF
INTERNAL
CONSISTENCY
9.6
DIAGNOSTICS
OF
OPTIMALITY
OF
ASSIMILATION
ALGORITHMS
9.7
CONCLUSIONS
ACKNOWLEDGEMENTS
REFERENCES
ERROR
DYNAMICS
IN
ENSEMTRLE
KALMAN-FILTER
SYSTEMS:
P.
HOUTEKAMER
10.1
MOTIVATION
10.2
ESTIMATION
OF
SCALARS
AND MATRICES
10.3
ASSIMILATION
OF
ONE
OBSERVATION
10.4
EXPERIMENTS
WITH
THE
LORENZ
III
MODEL
10.5
DISCUSSION
REFERENCES
SHORT-RANGE
ERROR
STATISTICS
IN
AN
ENSEMBLE
KALMAN
P.
HOUTEKAMER
11.1
INTRODUCTION
11.2
EXPERIMENTAL
ENVIRONMENT
11.3
HORIZONTAL
CORRELATIONS
11.4
VERTIIAL
CORRELATIONS
11.5 TEMPORAL
CORRELATIONS
11.6
STRATOSPHERIC
WIND
ANALYSIS
11.7
DISCUSSION
REFERENCES
ERROR
DYNAMICS
IN
ENSEMBLE
KALMAN
FILTER
SYSTEMS:
P.
HOUTEKAMER
12.1
INTRODUCTION
12.2
MONTE
CARLO
METHODS
12.3
REVIEW
OF
MODEL ERROR
12.4 REVIEW
OF
DATA-ASSIMILATION ERROR
12.5 EVIDENCE OF
BIAS
12.6 EVIDENCE OF
HORIZONTAL
ERROR
CORRELATIONS
12.7
DISCUSSION
REFERENCES
PARTICLE
FILTERS
FOR THE
GEOSCIENCES
P.
J.
VAN
LEEUWEN
13.1
INTRODUCTION
L3.2
A
SIMPLE
PARTICLE
FILTER
BASED
ON
IMPORTANCE
SAMPLING
CONTENTS
XV
,RO
23L
23T
235
QRN
ZOT
238
250
252
253
253
LOCALIZATION
255
257
257
258
260
264
264
FILTER
267
269
270
271
273
275
276
277
278
SYSTEM
ERROR
279
28T
28R
282
284
285
286
287
288
11
L2
29L
293
294
13
XVI
CONTENTS
13.3
REDUCING
THE VARIANCE
IN
THE
WEIGHTS
13.4
THE
PROPOSAL
DENSITY
13.5
CONCLUSIONS
REFERENCES
SECOND-ORDER
METHODS
FOR
ERROR
PROPAGATION
IN
VARIATIONAL
DATA ASSIMILATION
F.-X. LE
DIMET,
I.
GEJADZE,
AND
V.
SHUTYAEV
14.1
INTRODUCTION
14.2
YARIATIONAL
METHODS
14.3
SECOND-ORDER
METHODS
14.4
SENSITIVITY
WITH
RESPECT
TO
SOURCES
14.5
STOCHASTIC METHODS
14.6
COVARIANCES
OF
THE OPTIMAL
SOLUTION
ERROR
14.7
EFFECTIVE
INVERSE
HESSIAN
(EIH)
METHOD
14.8 NUMERICAL
EXAMPLES
14.9
CONCLUSIONS
ACKNOWLEDGEMENTS
REFERENCES
ADJOINTS
BY.
AUTOMATIC
DIFFERENTIATION
L.
HASCOET
15.1
INTRODUCTION
15.2
ELEMENTS
OF
AD
15.3
APPLICATION
OF ADJOINT
AD TO
DATA
ASSIMILATION
15.4
IMPROVING
THE ADJOINT
AD
CODE
15.5
AD
TOOLS
15.6
CONCLUSION
REFERENCES
ASSIMILATION
OF
IMAGES
A.
VIDARD,
O.
TITAUD
16.1
MOTIVATIONS
16.2
IMAGES:
LEVEL(S)
OF
INTERPRETATION
16.3
CURRENT
USE
OF
IMAGES
IN
DATA
ASSIMILATION:
PSEUDO
OBSERVATION
16.4
DIRECT
ASSIMILATION
OF
IMAGES
REFERENCES
MULTIGRID
ALGORITHMS
AND LOCAL
MESH
REFINEMENT
METHODS
IN
THE CONTEXT
OF VARIATIONAL
DATA ASSIMILATION
L.
DEBREU,
E.
NEVEU,
E.
SIMON,
AND
F.-X.
LE
DIMET
BGB
17.1
STRUCTURE
OF
THE VARIATIONAL
DATA
ASSIMILATION
PROBLEM
397
17.2
MULTIGRID
METHODS
AND
APPLICATION
TO
VARIATIONAL
DATA
ASSIMILATION
400
17.3
DATA
ASSIMILATION
AND
LOCAL
MESH
REFINEMENT
405
17.4
COUPLING
THE TWO
APPROACHES
409
17.5
CONCLUSIONS AND
PERSPECTIVES
4LO
REFERENCES
4LL
298
300
316
3I8
15
319
32L
322
325
329
334
335
338
347
346
347
347
349
351
351
359
362
364
366
368
37L
JIJ
375
OEL
I
382
391
16
L7
CONTENTS
XVII
L8
SELECTED
TOPICS
IN
MULTISCALE
DATA ASSIMILATION
M.
BOCQUET,
L.
WU,
F.
CHEVALLIER,
AND
M.
R. KOOKHAN
18.1 INTROCHRCTION
18.2
BAYESIAN
MULTISCALE
ANALYSIS
18.3
APPLICATION
TO
BAYESIAN
CONTROL
SPACE
DESIGN
18.4
EMPIRICAL
MULTISCALE STATISTICS
18.5
CONCHRSION
REFERENCES
DATA
ASSIMILATION
IN
METEOROLOGY
F.
RABIER
AND
M.
FISHER
19.1
TYANSFORMING
DATA
19.2
COMPARING
DATA
AND
MODELS
19.3
THINNING THE
DATASET
19.4
FILTERING
THE
ANALYSIS
19.5
NONLINEARITIES AND
NON-GAUSSIAN
DENSITIES
IN
VARIATIONAL
DATA
ASSIMILATION
19.6
PARALLEL
ALGORITHMS
FOR
4D-VAR
19.7
CONCLUSION
REFERENCES
AN
INTRODUCTION
TO
INVERSE
MODELLING
AND
PARAMETER
ESTIMATION
FOR
ATMOSPHERE AND
OCEAN
SCIENCES
M.
BOCQUET
20.
I
INTRODUCTION
20.2
BAYESIAN APPROACH
TO
INVERSE
PROBLEMS
20.3 ALTERNATIVE
APPROACHES
20.4 ESTIMATION
OF
SECOND-ORDER
STATISTICS
20.5
INVERSE
MODELLING
IN
ATMOSPHERIC,AND
OCEAN
SCIENCES:
A SELECTION
20.6
CONCLUSION
ACKNOWLEDGEMENTS
REFERENCES
GREENHOUSE
GAS
FLUX
INVERSION
F.
CHEVALLIER
21.1 INTRODUCTION
21.2
OBSERVATIONS
21.3
UNCERTAINTIES
21.4
METHODS
21.5
CONCLUSION
REFERENCES
DATA
ASSIMILATION
IN
ATMOSPHERIC CHEMISTRY AND
AIR
QUALITY
H.
ELBERN,
E.
FRIESE,
L.
NIERADZIK,
AND
J.
SCHWINGER
22.1
INTRODUCTION
19
4T3
475
475
422
427
430
431.
433
435
439
444
447
449
453
456
457
461
463
463
473
479
489
493
493
493
497
499
499
500
501
502
503
20
21
507
509
XVIII
CONTENTS
22.2
ADVANCED
CHEMISTRY
DATA
ASSIMILATION
22.3
A
POSTERIORI
VALIDATION
IN
ATMOSPHERIC
CHEMISTRY
22.4 TLOPOSPHERIC
CHEMICAL
DATA
ASSIMILATION
22.5
AEROSOL
DATA
ASSIMILATION
REFERENCES
23
COMTRINING
MODELS
AND
DATA
IN
LARGE-SCALE
OCEANOGRAPHY:
EXAMPLES
FROM
THE
CONSORTIUM
FOR
ESTIMATING
THE
CIRCULATION
AND
CLIMATE
OF
THE
OCEAN
(ECCO)
I.
FUKUMORI
535
23.1
INTRODUCTION
537
23.2
PHYSICAL
CONSISTENCY
SZT
23.3
ECCO
PRODUCTS
599
23.4
TRXAMPLES
OF
ECCO
APPLICATIONS
B40
23.5
PRACTICAL
CONSIDERATIONS
IN
EMPLOYING
ADVANCED
ESTIMATION
METHODS
B44
23.6
SUMMARY
550
ACKNOWLEDGEMENTS
551
REFERENCES
B51
24
DATA
ASSIMILATION
IN
COASTAL
OCEANOGRAPHY:
IS4DVAR
IN
THE
REGIONAL
OCEAN
MODELLING
SYSTEM
(ROMS)
J.
ZAI/ALA-GARAY,
J.
WILKIN,
AND
J. LEVIN
24.1 THE
REGIONAL
OCEAN
MODELLING
SYSTEM AND
THE
IS4DVAR
DATA
ASSIMILATION
ALGORITHM
SBT
24.2
ROMS IS4DVAR
IN
A
QUASI-GEOSTROPHIC
DOMAIN:
THE EAST
AUSTRALIA
CURRENT
24.3
ROMS IS4DVAR
IN
A
COMPLEX
COASTAL
DOMAIN:
THE MIDDLE
ATLANTIC
BIGHT
REFERENCES
DATA
ASSIMILATION
IN
GLACIOLOGY
B.
BONAN,
M.
NODET,
O.
OZENDA,
AND
C. P-ITZ
25.1
INTRODUCTION
25.2
ICE-SHEET
MODEL
25.3
ADJOINT
METHOD
AND
ADJOINT
MODEL
25.4
NUMERICAL
RESULTS
FOR
TWIN
EXPERIMENTS
REFERENCES
513
518
520
523
528
DDI)
560
564
J
R.)
OL
I
579
579
581
582
584 |
| any_adam_object | 1 |
| author2 | Blayo, Éric |
| author2_role | edt |
| author2_variant | é b éb |
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| author_facet | Blayo, Éric |
| building | Verbundindex |
| bvnumber | BV042636241 |
| classification_rvk | RB 10103 UD 4847 |
| ctrlnum | (OCoLC)912138984 (DE-599)BSZ416839037 |
| discipline | Physik Geographie |
| edition | 1. ed. |
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| genre_facet | Datensammlung Konferenzschrift 2012 Les Houches |
| id | DE-604.BV042636241 |
| illustrated | Illustrated |
| indexdate | 2024-08-08T00:10:30Z |
| institution | BVB |
| institution_GND | (DE-588)1064882943 |
| isbn | 9780198723844 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028068709 |
| oclc_num | 912138984 |
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| owner_facet | DE-19 DE-BY-UBM DE-20 DE-703 |
| physical | XXIV, 584 S. Ill., graph. Darst. |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Oxford University Press |
| record_format | marc |
| spelling | Advanced data assimilation for geosciences École de Physique des Houches, special Issue, 28 May-15 June 2012 ed. by Eric Blayo ... 1. ed. Oxford Oxford University Press 2015 XXIV, 584 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Geoinformatik (DE-588)7571300-7 gnd rswk-swf Daten (DE-588)4135391-2 gnd rswk-swf Geostatistik (DE-588)4020279-3 gnd rswk-swf Geowissenschaften (DE-588)4020288-4 gnd rswk-swf Datenauswertung (DE-588)4131193-0 gnd rswk-swf (DE-588)4148875-1 Datensammlung gnd-content (DE-588)1071861417 Konferenzschrift 2012 Les Houches gnd-content Geowissenschaften (DE-588)4020288-4 s Daten (DE-588)4135391-2 s Datenauswertung (DE-588)4131193-0 s DE-604 Geoinformatik (DE-588)7571300-7 s Geostatistik (DE-588)4020279-3 s Blayo, Éric (DE-588)1072659271 edt École de physique des Houches 99. 2012 Les Houches Sonstige (DE-588)1064882943 oth SWB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028068709&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Advanced data assimilation for geosciences École de Physique des Houches, special Issue, 28 May-15 June 2012 Geoinformatik (DE-588)7571300-7 gnd Daten (DE-588)4135391-2 gnd Geostatistik (DE-588)4020279-3 gnd Geowissenschaften (DE-588)4020288-4 gnd Datenauswertung (DE-588)4131193-0 gnd |
| subject_GND | (DE-588)7571300-7 (DE-588)4135391-2 (DE-588)4020279-3 (DE-588)4020288-4 (DE-588)4131193-0 (DE-588)4148875-1 (DE-588)1071861417 |
| title | Advanced data assimilation for geosciences École de Physique des Houches, special Issue, 28 May-15 June 2012 |
| title_auth | Advanced data assimilation for geosciences École de Physique des Houches, special Issue, 28 May-15 June 2012 |
| title_exact_search | Advanced data assimilation for geosciences École de Physique des Houches, special Issue, 28 May-15 June 2012 |
| title_full | Advanced data assimilation for geosciences École de Physique des Houches, special Issue, 28 May-15 June 2012 ed. by Eric Blayo ... |
| title_fullStr | Advanced data assimilation for geosciences École de Physique des Houches, special Issue, 28 May-15 June 2012 ed. by Eric Blayo ... |
| title_full_unstemmed | Advanced data assimilation for geosciences École de Physique des Houches, special Issue, 28 May-15 June 2012 ed. by Eric Blayo ... |
| title_short | Advanced data assimilation for geosciences |
| title_sort | advanced data assimilation for geosciences ecole de physique des houches special issue 28 may 15 june 2012 |
| title_sub | École de Physique des Houches, special Issue, 28 May-15 June 2012 |
| topic | Geoinformatik (DE-588)7571300-7 gnd Daten (DE-588)4135391-2 gnd Geostatistik (DE-588)4020279-3 gnd Geowissenschaften (DE-588)4020288-4 gnd Datenauswertung (DE-588)4131193-0 gnd |
| topic_facet | Geoinformatik Daten Geostatistik Geowissenschaften Datenauswertung Datensammlung Konferenzschrift 2012 Les Houches |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028068709&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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