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  • 1
    Call number: PIK N 456-18-91895 ; AWI A5-18-91895
    Type of Medium: Monograph available for loan
    Pages: xv, 569 Seiten , Illustrationen, Diagramme, Karten
    ISBN: 9780128117149
    Language: English
    Note: Contents: Contributors. - Preface. - Acknowledgements. - PART I SETTING THE SCENE. - 1. Introduction: Why Sub-seasonal to Seasonal Prediction (S2S)? / Frédéric Vitart, Andrew W. Robertson. - 1 History of Numerical Weather and Climate Forecasting. - 2 Sub-seasonal to Seasonal Forecasting. - 3 Recent National and International Efforts on Sub-seasonal to Seasonal Prediction. - 4 Structure of This Book. - 2. Weather Forecasting: What Sets the Forecast Skill Horizon? / Zoltan Toth, Roberto Buizza. - 1 Introduction. - 2 The Basics of Numerical Weather Prediction. - 3 The Evolution of NWP Technique. - 4 Enhancement of Predictable signals. - 5 Ensemble Techniques: Brief Introduction. - 6 Expanding the forecast skill Horizon. - 7 Concludmg Remarks: Lessons for S2S Forecasting. - Acknowledgements. - 3. Weather Within Climate: Sub-seasonal Predictability of Tropical Daily Rainfall Characteristics / Vincent Moron, Andrew W. Robertson, Lei Wang. - 1 Introduction. - 2 Data and Methods. - 3 Results. - 4 Discussion and Concluding Remarks. - 4. Identifying Wave Processes Associated With Predictability Across Time Scales: An Empirical Normal Mode Approach / Gilbert Brunet, John Methven. - 1 Introduction. - 2 Partitioning Atmospheric Behavior Using Its Conservation Properties. - 3 The ENM Approach to Observed Data and Models and Its Relevance to S2S Dynamics and Predictability. - 4 Conclusion. - Acknowledgments. - PART II SOURCES OF S2S PREDICTABILITY. - 5. The Madden-Julian Oscillation / Steven J. Woolnough. - 1 Introduction. - 2 The Real-Time Multivariate MJO Index. - 3 Observed MJO Structure. - 4 The Relationship Between the MJO and Tropical and Extratropical Weather. - 5 Theories and Mechanisms for MJO Initiation, Maintenance, and Propagation. - 6 The Representation of the MJO in Weather and Climate Models. - 7 MJO Prediction. - 8 Future Priorities for MJO Research for S2S Prediction. - Acknowledgments. - 6. Extratropical Sub-seasonal to Seasonal Oscillations and Multiple Regimes: The Dynamical Systems View / Michael Ghil, Andreas Groth, Dmitri Kondrashov, Andrew W. Robertson. - 1 Introduction and Motivation. - 2 Multiple Midlatitude Regimes and Low-Frequency Oscillations. - 3 Extratropical Oscillations in the S2S Band. - 4 Low-Order, Data-Driven Modeling, Dynamical Analysis, and Prediction. - 5 Concluding Remarks. - Acknowledgments. - 7. Tropical-Extratropical Interactions and Teleconnections / Hai Lin, Jorgen Frederiksen, David Straus, Christiana Stan. - 1 Introduction. - 2 Tropical Influence on the Extratropical Atmosphere. - 3 Extratropical Influence on the Tropics. - 4 Tropical-Extratropical, Two-Way Interactions. - 5 Summary and Discussion. - Appendix. Technical Matters Relating to Section 4.2. - 8. Land Surface Processes Relevant to Sub-seasonal to Seasonal (S2S) Prediction / Paul A. Dirmeyer, Pierre Gentine, Michael B. Ek, Gianpaolo Balsamo. - 1 Introduction. - 2 Process of Land-Atmosphere Interaction. - 3 A Brief History of Land-Surface Models. - 4 Predictability and Prediction. - 5 Improving Land-Driven Prediction. - 9. Midlatitude Mesoscale Ocean-Atmosphere Interaction and Its Relevance to S2S Prediction / R. Saravanan, P. Chang. - 1 Introduction. - 2 Data and Models. - 3 Mesoscale Ocean-Atmosphere Interaction in the Atmospheric Boundary Layer. - 4 Local Tropospheric Response. - 5 Remote Tropospheric Response. - 6 Impact on Ocean Circulation. - 7 Implications for S2S Prediction. - 8 Summary and Conclusions. - Acknowledgments. - 10. The Role of Sea Ice in Sub-seasonal Predictability / Matthieu Chevallier, François Massonnet, Helge Goessling, Virginie Guémas, Thomas Jung. - 1 Introduction. - 2 Sea Ice in the Coupled Atmosphere-Ocean System. - 3 Sea Ice Distribution, Seasonality, and Variability. - 4 Sources of Sea Ice Predictability at the Sub-seasonal to Seasonal Timescale. - 5 Sea Ice Sub-seasonal to Seasonal - Predictability and Prediction Skill in Models. - 6 Impact of Sea Ice on Sub-seasonal Predictability. - 7 Concluding Remarks. - Acknowledgments. - 11. Sub-seasonal Predictability and the Stratosphere / Amy Butler, Andrew Charlton-Perez, Daniela I. V. Domeisen, Chaim Garfinkel, Edwin P. Gerber, Peter Hitchcock, Alexey Yu. Karpechko, Amanda C. Maycock, Michael Sigmond, Isla Simpson, Seok-Woo Son. - 1 Introduction. - 2 Stratosphere-Troposphere Coup ling in the Tropics. - 3 Stratosphere-Troposphere Coupling in the Extratropics. - 4 Predictability Related to Extratropical Stratosphere-Troposphere Coupling. - 5 Summary and Outlook. - PART Ill S2S MODELING AND FORECASTING. - 12. Forecast System Design, Configuration, and Complexity / Yuhei Takaya. - 1 Introduction. - 2 Requirements and Constraints of the Operational Sub-seasonal Forecast. - 3 Effect of Ensemble Size and Lagged Ensemble. - 4 Real-Time Forecast Configuration. - 5 Reforecast Configuration. - 6 Summary and Concluding Remarks. - Acknowledgments. - 13. Ensemble Generation: The TIGGE and S2S Ensembles / Roberto Buizza. - 1 Global Sub-seasonal and Seasonal Prediction Is an Initial Value Problem. - 2 Ensembles Provide More Complete and Valuable Information Than Single States. - 3 A Brief Introduction to Data Assimilation. - 4 A Brief Introduction to Model Uncertainty Simulation. - 5 An Overview of Operational, Global, Sub-seasonal, and Seasonal Ensembles, and Their Initialization and Generation Methods. - 6 Ensembles: Considerations About Their Future. - 7 Summary and Key Lessons. - 14. GCMs With Full Representation of Cloud Microphysics and Their MJO Simulations / In-Sik Kang, Min-Seop Ahn, Hiroaki Miura, Aneesh Subramanian. - 1 Introduction. - 2 Global CRM. - 3 Superparameterized GCM. - 4 GCM With Full Representation of Cloud Microphysics and Scale-Adaptive Convection. - 5 Summary and Conclusion. - Acknowledgments. - 15. Forecast Recalibration and Multimodel Combination / Stefan Siegert, David B. Stephenson. - 1 Introduction. - 2 Statistical Methods for Forecast Recalibration. - 3 Regression Methods. - 4 Forecast Combination. - 5 Concluding Remarks. - Acknowledgments. - 16. Forecast Verification for S2S Timescales / Caio A. S. Coelho, Barbara Brown, Laurie Wilson, Marion Mittermaier, Barbara Casati. - 1 Introduction. - 2 Factors Affecting the Design of Verification Studies. - 3 Observational References. - 4 Review of the Most Common Verification Measures. - 5 Types of S2S Forecasts and Current Verification Practices. - 6 Summary, Challenges, and Recommendations in S2S Verification. - PART IV S2S APPLICATIONS. - 17. Sub-seasonal to Seasonal Prediction of Weather Extremes / Frédérik Vitart, Christopher Cunningham, Michael Deflorio, Emanuel Dutra, Laura Ferranti, Brian Golding, Debra Hudson, Charles Jones, Christophe Lavaysse, Joanne Robbins, Michael K. Tippett. - 1 Introduction. - 2 Prediction of Large-Scale, Long-Lasting Extreme Events. - 3 Prediction of Mesoscale Events. - 4 Display and Verification of Sub-seasonal Forecasts of Extreme Events. - 5 Conclusions. - 18. Pilot Experiences in Using Seamless Forecasts for Early Action: The "Ready-Set-Go!" Approach in the Red Cross / Juan Bazo, Roop Singh, Mathieu Destrooper, Erin Coughlan de Perez. - 1 Introduction. - 2 Why Sub-seasonal?. - 3 Case Study: Peru El Niño. - 4 Reflections on the Use of S2S Forecasts. - 5 Conclusions. - 19. Communication and Dissemination of Forecasts and Engaging User Communities / Joanne Robbins, Christopher Cunningham, Rutger Dankers, Matthew Degennaro, Giovanni Dolif, Robyn Duell, Victor Marchezini, Brian Mills, Juan Pablo Sarmiento, Amber Silver, Rachel Trajber, Andrew Watkins. - 1 Introduction. - 2 Sector-Specific Methods and Practices in S2S Forecast Communication, Dissemination, and Engagement. - 3 Guiding principles for improved communication Practices. - 4 Summary and Recommendations for Future Research. - 20. Seamless Prediction of Monsoon Onset and Active/Break Phases / A.
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  • 2
    Publication Date: 2013-09-20
    Description: Sub-seasonal forecasts have been routinely produced at ECMWF since 2002 with re-forecasts produced "on the fly" to calibrate the real-time sub-seasonal forecasts. In this study, the skill of the re-forecasts from April 2002 to March 2012 and covering a common set of years (1995 to 2001) has been evaluated. Results indicate that the skill of the ECMWF re-forecasts to predict the Madden Julian Oscillation has improved significantly since 2002, with an average gain of about 1 day of prediction skill per year. The amplitude of the MJO has also become more realistic, although the model still tends to produce MJOs which are weaker than in the ECMWF re-analysis. As a consequence, the ability of the ECMWF model to simulate realistic MJO teleconnections over the northern and southern Extratropics has improved dramatically over the 10-year period. Forecast skill scores have also improved in the Extratropics. For instance, weekly mean forecasts of the North Atlantic Oscillation Index are more skillful in recent years than ten years ago. A large part of this improvement seems to be linked to the improvements in the representation of the Madden Julian Oscillation. Skill to predict 2-metre temperature anomalies over the northern Extratropics has also improved almost continuously since 2002. Changes in the horizontal and vertical resolutions of the atmospheric model had only a small impact on the skill scores, suggesting that most of the improvements in the ECMWF sub-seasonal forecasts were due to changes in model physics which were primarily designed to improve the model climate and medium-range forecasts. The impact of changes in the data assimilation system and in the observing data has not been considered in this study, since all the re-forecasts used for this study were initialized from the same re-analysis over a common set of years.
    Print ISSN: 0035-9009
    Electronic ISSN: 1477-870X
    Topics: Geography , Physics
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  • 3
    Publication Date: 2015-04-11
    Description: Many theories for the Madden–Julian oscillation (MJO) focus on diabatic processes, particularly the evolution of vertical heating and moistening. Poor MJO performance in weather and climate models is often blamed on biases in these processes and their interactions with the large-scale circulation. We introduce one of three components of a model-evaluation project, which aims to connect MJO fidelity in models to their representations of several physical processes, focusing on diabatic heating and moistening. This component consists of 20-day hindcasts, initialised daily during two MJO events in winter 2009–10. The 13 models exhibit a range of skill: several have accurate forecasts to 20 days’ lead, while others perform similarly to statistical models (8–11 days). Models that maintain the observed MJO amplitude accurately predict propagation, but not vice versa. We find no link between hindcast fidelity and the precipitation–moisture relationship, in contrast to other recent studies. There is also no relationship between models’ performance and the evolution of their diabatic-heating profiles with rain rate. A more robust association emerges between models’ fidelity and net moistening: the highest-skill models show a clear transition from low-level moistening for light rainfall to mid-level moistening at moderate rainfall and upper-level moistening for heavy rainfall. The mid-level moistening, arising from both dynamics and physics, may be most important. Accurately representing many processes may be necessary, but not sufficient for capturing the MJO, which suggests that models fail to predict the MJO for a broad range of reasons and limits the possibility of finding a panacea.
    Print ISSN: 0148-0227
    Topics: Geosciences , Physics
    Published by Wiley on behalf of American Geophysical Union (AGU).
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  • 4
    Publication Date: 2015-10-13
    Description: There has been a great deal of recent interest in producing weather forecasts on the 2–6 week sub-seasonal timescale, which bridges the gap between medium-range (0–10 day) and seasonal (3–6 month) forecasts. While much of this interest is focused on the potential applications of skilful forecasts on the sub-seasonal range, understanding the potential sources of sub-seasonal forecast skill is a challenging and interesting problem, particularly because of the likely state-dependence of this skill (Hudson et al 2011). One such potential source of state-dependent skill for the Northern Hemisphere in winter is the occurrence of stratospheric sudden warming (SSW) events (Sigmond et al 2013). Here we show, by analysing a set of sub-seasonal hindcasts, that there is enhanced predictability of surface circulation not only when the stratospheric vortex is anomalously weak following SSWs but also when the vortex is extremely strong. Sub-seasonal forecasts initialized during st...
    Print ISSN: 1748-9318
    Electronic ISSN: 1748-9326
    Topics: Biology , Energy, Environment Protection, Nuclear Power Engineering
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  • 5
    Publication Date: 2015-04-11
    Description: An analysis of diabatic heating and moistening processes from 12–36 hour lead time forecasts from 12 Global Circulation Models are presented as part of the “Vertical structure and diabatic processes of the Madden-Julian Oscillation (MJO)” project. A lead time of 12–36 hours is chosen to constrain the large scale dynamics and thermodynamics to be close to observations while avoiding being too close to the initial spin-up of the models as they adjust to being driven from the YOTC analysis. A comparison of the vertical velocity and rainfall with the observations and YOTC analysis suggests that the phases of convection associated with the MJO are constrained in most models at this lead time although the rainfall in the suppressed phase is typically overestimated. Although the large scale dynamics is reasonably constrained, moistening and heating profiles have large inter-model spread. In particular, there are large spreads in convective heating and moistening at mid-levels during the transition to active convection. Radiative heating and cloud parameters have the largest relative spread across models at upper levels during the active phase. A detailed analysis of time step behaviour shows that some models show strong intermittency in rainfall and differences in the precipitation and dynamics relationship between models. The wealth of model outputs archived during this project is a very valuable resource for model developers beyond the study of the MJO. In addition, the findings of this study can inform the design of process model experiments, and inform the priorities for field experiments and future observing systems.
    Print ISSN: 0148-0227
    Topics: Geosciences , Physics
    Published by Wiley on behalf of American Geophysical Union (AGU).
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  • 6
    Publication Date: 2013-02-16
    Description: [1]  The Madden-Julian Oscillation (MJO) poses great challenges to our understanding and prediction of tropical convection and the large-scale circulation. Several internationally coordinated activities were recently formed to meet the challenges from the perspectives of numerical simulations, prediction, diagnostics, and virtual and actual field campaigns. This article provides a brief description of these activities and their connections, with the motivation in part to encourage the next generation of physical scientists to help solve the grand challengingproblem of the MJO.
    Print ISSN: 0094-8276
    Electronic ISSN: 1944-8007
    Topics: Geosciences , Physics
    Published by Wiley on behalf of American Geophysical Union (AGU).
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  • 7
    Publication Date: 2016-09-30
    Description: The prediction of precipitation by two ocean–atmosphere ensemble systems is compared with observations and each other over a broad range of time scales. The systems are the 2015 version of the Australian Bureau of Meteorology's POAMA with a T47 atmosphere, and the 2011 version of the European Centre for Medium Range Weather Forecasts’ (ECMWF) monthly system with a variable atmospheric resolution of T639 to T319. To facilitate the comparison across a seamless range of time scales, verification against observations is performed using data averaged over time windows equal in length to the forecast lead time, from 1 day to 4 weeks. In addition to this ‘actual’ skill, potential skill is computed by taking one ensemble member as truth and computing how well the other members forecast that member. Overall, ECMWF shows higher actual skill than POAMA across all time scales and in both the tropics and extratropics, as expected given its greater sophistication. ECMWF is particularly more skilful than POAMA in the tropics for the shorter leads. Consistent between the two systems, however, is that as lead time and averaging window are simultaneously increased the near-equatorial skill remains approximately constant, whereas it drops in all other latitude bands. As a result, both systems show much higher skill in the tropics than extratropics for the one week time scale and beyond, with that skill concentrated over the equatorial Pacific. Although potential skill in both systems is almost everywhere higher than their actual skill, there remains a strong similarity in the spatial patterns of potential and actual skill for the longer time scales. Within-model comparisons of potential and actual skill show largest differences for POAMA in the tropics at short lead times, and largest differences for ECMWF in the southern hemisphere high latitudes (50-70°S). The implications of these findings are discussed.
    Print ISSN: 0035-9009
    Electronic ISSN: 1477-870X
    Topics: Geography , Physics
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  • 8
    Publication Date: 2017-05-31
    Description: Several monsoon indices have been applied to multiple models from the sub-seasonal to Seasonal (S2S) prediction project database during the period May to October 1999–2010 to assess their ability to simulate the Asian monsoon. The Bivariate Anomaly Correlation (BAC) of Boreal Summer Intraseasonal Oscillation (BSISO) index suggests that the operational models can predict the BSISO1 and BSISO2 events up to 6 to 24.5 and 6.5 to 14 days in advance respectively, although the models tend to underestimate the amplitude of BSISO as the lead time increases. For the strong BSISO events, BSISO1 (BSISO2) display lower skill mostly in phases 3 to 5 for all the models, suggesting that the BSISO1 (BSISO2) is not easy to predict when it is located over India and the Maritime Continent (south China Sea and Bay of Bengal). On the other hand, the higher skills appear in different phases for different models. For instance, the limit of predictive skill of strong BSISO1 and BSISO2 events in phases 6–7 for the ECMWF ensemble forecast could exceed 30 days and 28 days, respectively. The comparisons of the BSISO life cycle among the ECMWF, NCEP and CMA models also indicate that the ECMWF model can better predict the evolution of strong BSISO events. Predictions of additional monsoon circulation indices including Webster-Yang index (WY), Indian summer monsoon index (ISM), the South Asian monsoon index (SAM), and the Southeast Asian monsoon index (SEAM) in the S2S models have statistically significant skill over the corresponding monsoon regions up to 9–31, 3–17, 7–13 and 7–14 days, respectively. However, the significant skill of summer monsoon precipitation over the SAM and SEAM regions varies significantly among the models, with the skill ranging from 2 days to 2 weeks lead time.
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    Topics: Geography , Physics
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  • 9
    Publication Date: 2017-06-15
    Description: Members in ensemble forecasts differ due to the representations of initial uncertainties and model uncertainties. The inclusion of stochastic schemes to represent model uncertainties has improved the probabilistic skill of the ECMWF ensemble by increasing reliability and reducing the error of the ensemble mean. Recent progress, challenges and future directions regarding stochastic representations of model uncertainties at ECMWF are described in this paper. The coming years are likely to see a further increase in the use of ensemble methods in forecasts and assimilation. This will put increasing demands on the methods used to perturb the forecast model. An area that is receiving a greater attention than 5 to 10 years ago is the physical consistency of the perturbations. Other areas where future efforts will be directed are the expansion of uncertainty representations to the dynamical core and to other components of the Earth system as well as the overall computational efficiency of representing model uncertainty.
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  • 10
    Publication Date: 2016-12-10
    Description: Uncertainties in parameterized processes in general circulation models can be represented as stochastic perturbations to the model formulation. The European Centre for Medium-Range Weather Forecasts (ECMWF) has pioneered approaches to represent these model errors in forecasting systems. In particular, the stochastically perturbed physical tendency (SPPT) scheme for the atmosphere is used in their operational ensemble system for medium- and long-range predictions. Recent studies have shown that these stochastic approaches can both increase the reliability of the probabilistic forecasts and reduce long-term mean biases of the model climate. Towards developing a seamless prediction system in the future, these benefits of stochastic parameterization for both short-term and long-term forecasts make it an essential component of the next generation Earth System Models. We present results of the impact of different configurations of the SPPT scheme in ECMWF’s seasonal forecasting System 4 on the mean and variability in tropical precipitation. Small scale perturbations in the SPPT scheme play a significant role in reducing the mean biases in tropical precipitation. The stochastic physics also non-linearly rectifies the convection and precipitation during different phases of El Niño Southern Oscillation events and improves the reliability of the ensemble forecasts for the Madden-Julian Oscillation (MJO). They impact the MJO dynamics by modulating the convective and suppressed phases of the MJO. Finally, we discuss some of the caveats to this analysis and some future prospects.
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    Topics: Geography , Physics
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