Book contents
- Frontmatter
- Contents
- List of contributors
- Preface
- Chapter 1 Predictability of weather and climate: from theory to practice
- Chapter 2 Predictability from a dynamical meteorological perspective
- Chapter 3 Predictability – a problem partly solved
- Chapter 4 The Liouville equation and atmospheric predictability
- Chapter 5 Application of generalised stability theory to deterministic and statistical prediction
- Chapter 6 Ensemble-based atmospheric data assimilation
- Chapter 7 Ensemble forecasting and data assimilation: two problems with the same solution?
- Chapter 8 Approximating optimal state estimation
- Chapter 9 Predictability past, predictability present
- Chapter 10 Predictability of coupled processes
- Chapter 11 Predictability of tropical intraseasonal variability
- Chapter 12 Predictability of seasonal climate variations: a pedagogical review
- Chapter 13 Predictability of the North Atlantic thermohaline circulation
- Chapter 14 On the predictability of flow-regime properties on interannual to interdecadal timescales
- Chapter 15 Model error in weather and climate forecasting
- Chapter 16 Observations, assimilation and the improvement of global weather prediction – some results from operational forecasting and ERA-40
- Chapter 17 The ECMWF Ensemble Prediction System
- Chapter 18 Limited-area ensemble forecasting: the COSMO-LEPS system
- Chapter 19 Operational seasonal prediction
- Chapter 20 Weather and seasonal climate forecasts using the superensemble approach
- Chapter 21 Predictability and targeted observations
- Chapter 22 The attributes of forecast systems: a general framework for the evaluation and calibration of weather forecasts
- Chapter 23 Predictability from a forecast provider's perspective
- Chapter 24 Ensemble forecasts: can they provide useful early warnings?
- Chapter 25 Predictability and economic value
- Chapter 26 A three-tier overlapping prediction scheme: tools for strategic and tactical decisions in the developing world
- Chapter 27 DEMETER and the application of seasonal forecasts
- Index
- Plate section
- References
Chapter 9 - Predictability past, predictability present
Published online by Cambridge University Press: 03 December 2009
Book contents
- Frontmatter
- Contents
- List of contributors
- Preface
- Chapter 1 Predictability of weather and climate: from theory to practice
- Chapter 2 Predictability from a dynamical meteorological perspective
- Chapter 3 Predictability – a problem partly solved
- Chapter 4 The Liouville equation and atmospheric predictability
- Chapter 5 Application of generalised stability theory to deterministic and statistical prediction
- Chapter 6 Ensemble-based atmospheric data assimilation
- Chapter 7 Ensemble forecasting and data assimilation: two problems with the same solution?
- Chapter 8 Approximating optimal state estimation
- Chapter 9 Predictability past, predictability present
- Chapter 10 Predictability of coupled processes
- Chapter 11 Predictability of tropical intraseasonal variability
- Chapter 12 Predictability of seasonal climate variations: a pedagogical review
- Chapter 13 Predictability of the North Atlantic thermohaline circulation
- Chapter 14 On the predictability of flow-regime properties on interannual to interdecadal timescales
- Chapter 15 Model error in weather and climate forecasting
- Chapter 16 Observations, assimilation and the improvement of global weather prediction – some results from operational forecasting and ERA-40
- Chapter 17 The ECMWF Ensemble Prediction System
- Chapter 18 Limited-area ensemble forecasting: the COSMO-LEPS system
- Chapter 19 Operational seasonal prediction
- Chapter 20 Weather and seasonal climate forecasts using the superensemble approach
- Chapter 21 Predictability and targeted observations
- Chapter 22 The attributes of forecast systems: a general framework for the evaluation and calibration of weather forecasts
- Chapter 23 Predictability from a forecast provider's perspective
- Chapter 24 Ensemble forecasts: can they provide useful early warnings?
- Chapter 25 Predictability and economic value
- Chapter 26 A three-tier overlapping prediction scheme: tools for strategic and tactical decisions in the developing world
- Chapter 27 DEMETER and the application of seasonal forecasts
- Index
- Plate section
- References
Summary
Maybe we oughta help him see,
The future ain't what it used to be.
Tom PettyIntroduction
Predictability evolves. The relation between our models and reality is one of similarity, not identity, and predictability takes form only within the context of our models. Thus predictability is a function of our understanding, our technology and our dedication to the task. The imperfection of our models implies that theoretical limits to predictability in the present may be surpassed; they need not limit predictability in the future. How then are we to exploit probabilistic forecasts extracted from our models, along with observations of the single realisation corresponding to each forecast, to improve the structure and formulation of our models? Can we exploit observations as one agent of a natural selection and happily allow our understanding to evolve without any ultimate goal, giving up the common vision of slowly approaching the Perfect Model? This chapter addresses these questions in a rather applied manner, and it adds a fourth: Might the mirage of a Perfect Model actually impede model improvement?
Given a mathematical dynamical system, a measurement function that translates between states of this system and observations, and knowledge of the statistical characteristics of any observational noise, then in principle we can quantify predictability quite accurately. But this situation describes the perfect model scenario (PMS), not the real world. In the real world we define the predictability of physical systems through our mathematical theories and our in silico models.
- Type
- Chapter
- Information
- Predictability of Weather and Climate , pp. 217 - 250Publisher: Cambridge University PressPrint publication year: 2006
References
and (2004). Using ensemble weather forecasts to manage utilities risk. Environ. Finance, 20, 8–9Google Scholar
(1919). Probability and practical weather forecasting. Centraltryckeriet Tecknologforeningens ForlagGoogle Scholar
(2002). Towards an alternative blueprint for a physically-based digitally simulated hydrologic response modelling system. Hydrol. Process., 16(2), 189–206CrossRefGoogle Scholar
, and (1999). Stochastic simulation of model uncertainties in the ECMWF Ensemble Prediction System. Quart. J. Roy Meteor. Soc., 125, 2887–908CrossRefGoogle Scholar
, and (2001). Linear regime duration: is 24 hours a long time in synoptic weather forecasting?J. Atmos. Sci., 22, 3525–392.0.CO;2>CrossRefGoogle Scholar
and (2005). Weather forecasting for combined Cycle Gas Turbine (CCGT). Proceedings of the First THORPEX Science Symposium (in press). American Meteorological SocietyGoogle Scholar
and (2000). The role of operational constraints in selecting supplementary observations. J. Atmos. Sci. 57(17), 2859–712.0.CO;2>CrossRefGoogle Scholar
, and (1997). Verification of the ECMWF wave forecasting system buoy and altimeter data. Weather Forecast., 12, 763–842.0.CO;2>CrossRefGoogle Scholar
(2003). Nonlinear state estimation, indistinguishable states and the extended Kalman filter, Physica D, 183, 273–81CrossRefGoogle Scholar
and (2001). Indistinguishable states. I: The perfect model scenario. Physica D, 125–41Google Scholar
and (2004). Indistinguishable states. II: Imperfect model scenario. Physica D, 196, 224–42Google Scholar
(1956). A new interpretation of information rate. Bell System Technical J., 35(4), 917–26CrossRefGoogle Scholar
and (2001). Bayesian calibration of computer codes. J. Roy. Stat. Soc. B, 63, 425–64CrossRefGoogle Scholar
(2002). Measuring dynamical prediction utility using relative entropy. J. Atmos. Sci., 50, 2057–722.0.CO;2>CrossRefGoogle Scholar
, et al. (1999). Improved skills for weather and seasonal climate forecasts from multimodel superensemble. Science, Sept 3Google Scholar
(1985). The growth of errors in prediction. In Turbulence and Predictability in Geophysical Fluid Dynamics and Climate Dynamics, ed. , pp. 243–65. North HollandGoogle Scholar
and (1998). Optimal sites for supplementary weather observations. J. Atmos. Sci., 55, 399–4142.0.CO;2>CrossRefGoogle Scholar
(1977). The value of climatological, categorical and probabilistic forecasts in the cost-loss ratio situation. Mon. Weather Rev., 105, 803–162.0.CO;2>CrossRefGoogle Scholar
, , and (2001). Model error and operational weather forecasts. Nonlinear Proc. Geoph., 8, 357–71CrossRefGoogle Scholar
(2001). A nonlinear perspective on model error. Quart. J. Roy. Meteor. Soc., 127, 279–304Google Scholar
(2002). The economic value of ensemble forecasts as a tool for risk assessment: from days to decades. Quart. J. Roy. Meteor. Soc., 128, 747–74CrossRefGoogle Scholar
(2000). Skill and relative economic value of the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 126, 649–68CrossRefGoogle Scholar
and (2002). Evaluating probabilistic forecasts using information theory. Mon. Weather Rev., 130(6), 1653–602.0.CO;2>CrossRefGoogle Scholar
and (2004). The boy who cried wolf revisited: the impact of false alarm intolerance on cost-loss scenarios. Weather Forecast., 19(2), 391–72.0.CO;2>CrossRefGoogle Scholar
, and (2001). A Forecast Reliability Index from Ensembles: A Comparison of Methods. Report. Deutscher WetterdienstGoogle Scholar
, , and (2003). Using medium range weather forecasts to improve the value of wind energy production. Renewable Energy, 28(4), 585–602CrossRefGoogle Scholar
, , J. von Hardenberg and (2005). Forecasting wave height probabilities with numerical weather prediction models. Ocean Eng., 32, 1841–63CrossRefGoogle Scholar
(1996). Accountability and error in forecasts. In Proceedings of the 1995 Predictability Seminar, ECMWF
(1997). The Maintenance of Uncertainty. In Proceedings of the International School of Physics (Enrico Fermi), Course ⅭXXXIII, ed. G. Cini (Societa Italiana di Fisica, Bologna), pp. 177–368Google Scholar
(2001). Disentangling uncertainty and error: on the predictability of nonlinear systems. In Nonlinear Dynamics and Statistics, ed. , pp. 31–64, Boston: BirkhauserGoogle Scholar
(2002). What might we learn from climate forecasts?Proc. Nat. Acad. Sci., 99, 2487–92CrossRefGoogle ScholarPubMed
, and (2001). End to End Ensemble Forecasting: Towards Evaluating the Economic Value of the Ensemble Prediction System. Technical Memorandum 336. ECMWFGoogle Scholar
, and (2005). Predictive distributions from an ensemble of weather forecasts: extracting California electricity demand from imperfect weather models. Physica D (in press)Google Scholar
, , , et al. (2005). Evaluating uncertainty in the climate response to changing levels of greenhouse gases. Nature 433(7024), 403–6CrossRefGoogle Scholar
, L. A. Smith and K. Judd (2005). A new view of seasonal forecast skill: bounding boxes from the DEMETER ensemble forecasts. Tellus, 57A, 265–79CrossRef
- 7
- Cited by