Muutke küpsiste eelistusi

E-raamat: Empirical Methods in Short-Term Climate Prediction

5.00/5 (2 hinnangut Goodreads-ist)
(Principal Scientist, CPC and Adjunct Professor, University of Maryland)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 07-Dec-2006
  • Kirjastus: Oxford University Press
  • Keel: eng
  • ISBN-13: 9780191513954
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 109,19 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Empirical Methods in Short-Term Climate PredictionSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 07-Dec-2006
  • Kirjastus: Oxford University Press
  • Keel: eng
  • ISBN-13: 9780191513954
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

This clear and accessible text describes the methods underlying short-term climate prediction at time scales of 2 weeks to a year. Although a difficult range to forecast accurately, there have been several important advances in the last ten years, most notably in understanding ocean-atmosphere interaction (El Nino for example), the release of global coverage data sets, and in prediction methods themselves. With an emphasis on the empirical approach, the text covers in detail empirical wave propagation, teleconnections, empirical orthogonal functions, and constructed analogue. It also provides a detailed description of nearly all methods used operationally in long-lead seasonal forecasts, with new examples and illustrations. The challenges of making a real time forecast are discussed, including protocol, format, and perceptions about users. Based where possible on global data sets, illustrations are not limited to the Northern Hemisphere, but include several examples from the Southern Hemisphere. Includes foreword by Professor Edward Lorenz (Massachusetts Institute of Technology).

Arvustused

Thoughtful, original, and worthy of serious consideration * Daniel S. Wilks, Professor of Atmospheric Science in the Department of Earth & Atmospheric Sciences at Cornell University in Ithaca, New York * This is a great book ... every student and practitioner of climate prediction should have a copy on their desk * The Geographical Journal *

Foreword by Edward N. Lorenz xi
Preface xiii
Acronyms and notions xv
List of Plates xvii
List of symbols xxi
Chapter
1. Introduction		 1
Chapter
2. Background on Orthogonal Functions and Covariance		 7
2.1 Orthogonal functions
7
2.2 Correlation and covariance
10
2.3 Issues about removal of "the mean"
12
2.4 Concluding remarks
13
Appendix: The anomaly correlation
14
Chapter
3. Empirical Wave Propagation		 17
3.1 Data and EWP method
18
3.1.1 Data treatment
18
3.1.2 Amplitude
18
3.1.3 Phase shifting
19
3.1.4 Mean propagation
20
3.1.5 EWP forecast method
21
3.2 EWP diagnostics
22
3.3 Rock in the pond experiments
25
3.4 Skill of EWP one-day forecasts
28
3.5 Discussion of EWP
30
3.5.1 Eulerian and Lagrangian persistence
30
3.5.2 Reversing time and targeted observations
31
3.5.3 Application of EWP
31
3.5.4 Historical note
33
3.5.5 Weak points of EWP
34
Appendix 1: EWP formal derivation
34
Appendix 2: The Rossby equation
36
Chapter
4. Teleconnections		 37
4.1 Working definition
38
4.2 Two most famous examples in NH
38
4.3 The measure of teleconnection
42
4.4 Finding teleconnections systematically (EOT)
44
4.5 Discussion
47
4.6 Monitoring, indices and station data
50
4.7 Closing comment
51
Chapter
5. Empirical Orthogonal Functions		 53
5.1 Methods and definitions
54
5.1.1 Working definition
54
5.1.2 The covariance matrix
54
5.1.3 The alternative covariance matrix
54
5.1.4 The covariance matrix: context
55
5.1.5 EOF through eigenanalysis
56
5.1.6 Explained variance (EV)
57
5.2 Examples
58
5.3 Simplification of EOF-EOT
63
5.4 Discussion of EOF
66
5.4.1 Summary of procedures and properties
66
5.4.2 The spectrum
69
5.4.3 Interpretation of EOF
70
5.4.4 Reproducibility (sampling variability)
70
5.4.5 Variations on the EOF theme
70
5.4.6 EOF in models
71
5.4.7 More examples
72
5.4.8 Common misunderstandings
75
5.4.9 Closing comment
76
Appendix 1: Post processing
76
Appendix 2: Iteration
77
Chapter
6. Degrees of Freedom		 79
6.1 Methods to estimate effective degrees of freedom, N
80
6.2 Example
81
6.3 Link of degrees of freedom to EOF
83
6.4 Remaining questions
84
6.5 Concluding comments
85
Chapter
7. Analogues		 87
7.1 Natural analogues (NA)
88
7.1.1 Similarity measures
90
7.1.2 Search for 500 mb height analogues
91
7.1.3 How long do we have to wait?
94
7.1.4 Application of natural analogues
96
7.2 Constructed analogues
97
7.2.1 The idea
97
7.2.2 The method of finding the weights αj
99
7.2.3 Example of the weights
100
7.3 Specification or downscaling
102
7.4 Global seasonal SST forecasts
104
7.5 Short-range forecasts and dispersion experiments
108
7.5.1 Short-range forecasts
108
7.5.2 CA dispersion experiment
110
7.6 Calculating the fastest growing modes by empirical means
111
7.6.1 Growing modes
113
7.6.2 Example
114
7.6.3 Discussion of growing modes
116
Appendix: Forecasts with CA
117
Chapter
8. Methods in Short-Term Climate Prediction		 121
8.1 Climatology
122
8.2 Persistence
124
8.3 Optimal climate normals
126
8.4 Local regression
129
8.5 Non-local regression and ENSO
135
8.6 Composites
138
8.7 Regression on the pattern level
139
8.7.1 The time-lagged covariance matrix
139
8.7.2 CCA, SVD and EOT2
141
8.7.3 LIM, POP and Markov
145
8.8 Numerical methods
146
8.9 Consolidation
147
8.10 Other methods
149
8.11 Methods not used
151
Appendix 1: Some practical space–time continuity requirements
152
Appendix 2: Consolidation by ridge regression
153
Chapter
9. The Practice of Short-Term Climate Prediction		 157
9.1 On the seasonal mean
158
9.2 Lay-out of the forecasts
159
9.3 Time-scales in the seasonal forecast
160
9.4 Which elements are forecast, and by which methods?
161
9.5 Expressing uncertainty
163
9.6 Simplifications of the probability forecast (the three classes)
164
9.7 Format of the forecast
167
9.8 The official forecast
169
9.9 Verification 1: a priori skill and hindcasts
170
9.10 Verification 2: Heidke skill scores
173
9.11 Trends
175
9.12 Forecasts of opportunity (and the tension with regularly scheduled operations)
176
Appendix: Historical notes
177
Chapter
10. Conclusion		 179
10.1 Linearity
180
10.2 Relative performance GCMs and empirical methods
182
10.3 Predictability
185
10.4 The future of short-term climate prediction
187
References 189
Index 201


Huug van den Dool gained his PhD in Dynamical Meteorology from the University of Utrecht in 1975. He has since worked as a researcher at KNMI (Royal Netherlands Meteorological Institute), Scripps Institution of Oceanography, the University of Maryland, and as Chief of Prediction at the CPC (Climate Prediction Center). He is currently a principal scientist at CPC and an adjunct professor at the University of Maryland.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97801915139542e.html
Märksõnad: