Empirical Methods in Short-Term Climate Prediction.

By: Dool, Huug van denMaterial type: TextTextPublisher: Oxford : Oxford University Press, 2006Copyright date: ©2007Description: 1 online resource (252 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9780191513954Subject(s): Long-range weather forecasting -- Mathematical models | PhysicsGenre/Form: Electronic books.Additional physical formats: Print version:: Empirical Methods in Short-Term Climate PredictionDDC classification: 551.63 LOC classification: QC997.V35 2007Online resources: Click to View
Contents:
Intro -- Table of Contents -- Foreword -- Preface -- Acronyms and notions -- List of Plates -- List of symbols -- Chapter 1. Introduction -- Chapter 2. Background on Orthogonal Functions and Covariance -- 2.1 Orthogonal functions -- 2.2 Correlation and covariance -- 2.3 Issues about removal of ''the mean'' -- 2.4 Concluding remarks -- Appendix: The anomaly correlation -- Chapter 3. Empirical Wave Propagation -- 3.1 Data and EWP method -- 3.1.1 Data treatment -- 3.1.2 Amplitude -- 3.1.3 Phase shifting -- 3.1.4 Mean propagation -- 3.1.5 EWP forecast method -- 3.2 EWP diagnostics -- 3.3 Rock in the pond experiments -- 3.4 Skill of EWP one-day forecasts -- 3.5 Discussion of EWP -- 3.5.1 Eulerian and Lagrangian persistence -- 3.5.2 Reversing time and targeted observations -- 3.5.3 Application of EWP -- 3.5.4 Historical note -- 3.5.5 Weak points of EWP -- Appendix 1: EWP formal derivation -- Appendix 2: The Rossby equation -- Chapter 4. Teleconnections -- 4.1 Working definition -- 4.2 Two most famous examples in NH -- 4.3 The measure of teleconnection -- 4.4 Finding teleconnections systematically (EOT) -- 4.5 Discussion -- 4.6 Monitoring, indices and station data -- 4.7 Closing comment -- Chapter 5. Empirical Orthogonal Functions -- 5.1 Methods and definitions -- 5.1.1 Working definition -- 5.1.2 The covariance matrix -- 5.1.3 The alternative covariance matrix -- 5.1.4 The covariance matrix: context -- 5.1.5 EOF through eigenanalysis -- 5.1.6 Explained variance (EV) -- 5.2 Examples -- 5.3 Simplification of EOF-EOT -- 5.4 Discussion of EOF -- 5.4.1 Summary of procedures and properties -- 5.4.2 The spectrum -- 5.4.3 Interpretation of EOF -- 5.4.4 Reproducibility (sampling variability) -- 5.4.5 Variations on the EOF theme -- 5.4.6 EOF in models -- 5.4.7 More examples -- 5.4.8 Common misunderstandings -- 5.4.9 Closing comment -- Appendix 1: Post processing.
Appendix 2: Iteration -- Chapter 6. Degrees of Freedom -- 6.1 Methods to estimate effective degrees of freedom, N -- 6.2 Example -- 6.3 Link of degrees of freedom to EOF -- 6.4 Remaining questions -- 6.5 Concluding comments -- Chapter 7. Analogues -- 7.1 Natural analogues (NA) -- 7.1.1 Similarity measures -- 7.1.2 Search for 500 mb height analogues -- 7.1.3 How long do we have to wait? -- 7.1.4 Application of natural analogues -- 7.2 Constructed analogues -- 7.2.1 The idea -- 7.2.2 The method of finding the weights α[sub(j)] -- 7.2.3 Example of the weights -- 7.3 Specification or downscaling -- 7.4 Global seasonal SST forecasts -- 7.5 Short-range forecasts and dispersion experiments -- 7.5.1 Short-range forecasts -- 7.5.2 CA dispersion experiment -- 7.6 Calculating the fastest growing modes by empirical means -- 7.6.1 Growing modes -- 7.6.2 Example -- 7.6.3 Discussion of growing modes -- Appendix: Forecasts with CA -- Chapter 8. Methods in Short-Term Climate Prediction -- 8.1 Climatology -- 8.2 Persistence -- 8.3 Optimal climate normals -- 8.4 Local regression -- 8.5 Non-local regression and ENSO -- 8.6 Composites -- 8.7 Regression on the pattern level -- 8.7.1 The time-lagged covariance matrix -- 8.7.2 CCA, SVD and EOT2 -- 8.7.3 LIM, POP and Markov -- 8.8 Numerical methods -- 8.9 Consolidation -- 8.10 Other methods -- 8.11 Methods not used -- Appendix 1: Some practical space-time continuity requirements -- Appendix 2: Consolidation by ridge regression -- Chapter 9. The Practice of Short-Term Climate Prediction -- 9.1 On the seasonal mean -- 9.2 Lay-out of the forecasts -- 9.3 Time-scales in the seasonal forecast -- 9.4 Which elements are forecast, and by which methods? -- 9.5 Expressing uncertainty -- 9.6 Simplifications of the probability forecast (the three classes) -- 9.7 Format of the forecast -- 9.8 The official forecast.
9.9 Verification 1: a priori skill and hindcasts -- 9.10 Verification 2: Heidke skill scores -- 9.11 Trends -- 9.12 Forecasts of opportunity (and the tension with regularly scheduled opertions) -- Appendix: Historical notes -- Chapter 10. Conclusion -- 10.1 Linearity -- 10.2 Relative performance GCMs and empirical methods -- 10.3 Predictability -- 10.4 The future of short-term climate prediction -- References -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- X -- Y -- Z.
Summary: This clear, accessible text describes the methods and advances in short-term climate prediction at time scales of 2 weeks to a year. With an emphasis on the prediction methods themselves and the use of observations, the text is ideal for students and researchers in Meteorology, Atmospheric Science, Geoscience, Mathematics, Statistics and Physics.
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Intro -- Table of Contents -- Foreword -- Preface -- Acronyms and notions -- List of Plates -- List of symbols -- Chapter 1. Introduction -- Chapter 2. Background on Orthogonal Functions and Covariance -- 2.1 Orthogonal functions -- 2.2 Correlation and covariance -- 2.3 Issues about removal of ''the mean'' -- 2.4 Concluding remarks -- Appendix: The anomaly correlation -- Chapter 3. Empirical Wave Propagation -- 3.1 Data and EWP method -- 3.1.1 Data treatment -- 3.1.2 Amplitude -- 3.1.3 Phase shifting -- 3.1.4 Mean propagation -- 3.1.5 EWP forecast method -- 3.2 EWP diagnostics -- 3.3 Rock in the pond experiments -- 3.4 Skill of EWP one-day forecasts -- 3.5 Discussion of EWP -- 3.5.1 Eulerian and Lagrangian persistence -- 3.5.2 Reversing time and targeted observations -- 3.5.3 Application of EWP -- 3.5.4 Historical note -- 3.5.5 Weak points of EWP -- Appendix 1: EWP formal derivation -- Appendix 2: The Rossby equation -- Chapter 4. Teleconnections -- 4.1 Working definition -- 4.2 Two most famous examples in NH -- 4.3 The measure of teleconnection -- 4.4 Finding teleconnections systematically (EOT) -- 4.5 Discussion -- 4.6 Monitoring, indices and station data -- 4.7 Closing comment -- Chapter 5. Empirical Orthogonal Functions -- 5.1 Methods and definitions -- 5.1.1 Working definition -- 5.1.2 The covariance matrix -- 5.1.3 The alternative covariance matrix -- 5.1.4 The covariance matrix: context -- 5.1.5 EOF through eigenanalysis -- 5.1.6 Explained variance (EV) -- 5.2 Examples -- 5.3 Simplification of EOF-EOT -- 5.4 Discussion of EOF -- 5.4.1 Summary of procedures and properties -- 5.4.2 The spectrum -- 5.4.3 Interpretation of EOF -- 5.4.4 Reproducibility (sampling variability) -- 5.4.5 Variations on the EOF theme -- 5.4.6 EOF in models -- 5.4.7 More examples -- 5.4.8 Common misunderstandings -- 5.4.9 Closing comment -- Appendix 1: Post processing.

Appendix 2: Iteration -- Chapter 6. Degrees of Freedom -- 6.1 Methods to estimate effective degrees of freedom, N -- 6.2 Example -- 6.3 Link of degrees of freedom to EOF -- 6.4 Remaining questions -- 6.5 Concluding comments -- Chapter 7. Analogues -- 7.1 Natural analogues (NA) -- 7.1.1 Similarity measures -- 7.1.2 Search for 500 mb height analogues -- 7.1.3 How long do we have to wait? -- 7.1.4 Application of natural analogues -- 7.2 Constructed analogues -- 7.2.1 The idea -- 7.2.2 The method of finding the weights α[sub(j)] -- 7.2.3 Example of the weights -- 7.3 Specification or downscaling -- 7.4 Global seasonal SST forecasts -- 7.5 Short-range forecasts and dispersion experiments -- 7.5.1 Short-range forecasts -- 7.5.2 CA dispersion experiment -- 7.6 Calculating the fastest growing modes by empirical means -- 7.6.1 Growing modes -- 7.6.2 Example -- 7.6.3 Discussion of growing modes -- Appendix: Forecasts with CA -- Chapter 8. Methods in Short-Term Climate Prediction -- 8.1 Climatology -- 8.2 Persistence -- 8.3 Optimal climate normals -- 8.4 Local regression -- 8.5 Non-local regression and ENSO -- 8.6 Composites -- 8.7 Regression on the pattern level -- 8.7.1 The time-lagged covariance matrix -- 8.7.2 CCA, SVD and EOT2 -- 8.7.3 LIM, POP and Markov -- 8.8 Numerical methods -- 8.9 Consolidation -- 8.10 Other methods -- 8.11 Methods not used -- Appendix 1: Some practical space-time continuity requirements -- Appendix 2: Consolidation by ridge regression -- Chapter 9. The Practice of Short-Term Climate Prediction -- 9.1 On the seasonal mean -- 9.2 Lay-out of the forecasts -- 9.3 Time-scales in the seasonal forecast -- 9.4 Which elements are forecast, and by which methods? -- 9.5 Expressing uncertainty -- 9.6 Simplifications of the probability forecast (the three classes) -- 9.7 Format of the forecast -- 9.8 The official forecast.

9.9 Verification 1: a priori skill and hindcasts -- 9.10 Verification 2: Heidke skill scores -- 9.11 Trends -- 9.12 Forecasts of opportunity (and the tension with regularly scheduled opertions) -- Appendix: Historical notes -- Chapter 10. Conclusion -- 10.1 Linearity -- 10.2 Relative performance GCMs and empirical methods -- 10.3 Predictability -- 10.4 The future of short-term climate prediction -- References -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- X -- Y -- Z.

This clear, accessible text describes the methods and advances in short-term climate prediction at time scales of 2 weeks to a year. With an emphasis on the prediction methods themselves and the use of observations, the text is ideal for students and researchers in Meteorology, Atmospheric Science, Geoscience, Mathematics, Statistics and Physics.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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