In this paper the concept of Chaos and its applications to the study of predictability theory is introduced.The author's attempt is to give a general overview of ideas and methods involved in this problem to scien...In this paper the concept of Chaos and its applications to the study of predictability theory is introduced.The author's attempt is to give a general overview of ideas and methods involved in this problem to scientists,who are interested in the problem of predictability but not familiar with the theory of chaos.The problem is discussed in 4 sections.In the first section,the concept of chaos and the study methods are outlined briefly;in the second section,the methods of quantitatively measuring the main characteristics of chaos which are the basis for the predictability theory are introduced;the third section discusses the time series analysis for directly studying chaotic phenomena in practical problems;and the last section presents some research results on the chaotic characteristics and the predictability of the real atmosphere.展开更多
The North Atlantic Oscillation(NAO)is the most prominent mode of atmospheric variability in the Northern Hemisphere.Because of the close relationship between the NAO and regional climate in Eurasia,North Atlantic,and ...The North Atlantic Oscillation(NAO)is the most prominent mode of atmospheric variability in the Northern Hemisphere.Because of the close relationship between the NAO and regional climate in Eurasia,North Atlantic,and North America,improving the prediction skill for the NAO has attracted much attention.Previous studies that focused on the predictability of the NAO were often based upon simulations by climate models.In this study,the authors took advantage of Slow Feature Analysis to extract information on the driving forces from daily NAO index and introduced it into phase-space reconstruction.By computing the largest Lyapunov exponent,the authors found that the predictability of daily NAO index shows a significant increase when its driving force signal is considered.Furthermore,the authors conducted a short-term prediction for the NAO by using a global prediction model for chaotic time series that incorporated the driving-force information.Results showed that the prediction skill for the NAO can be largely increased.In addition,results from wavelet analysis suggested that the driving-force signal of the NAO is associated with three basic drivers:the annual cycle(1.02 yr),the quasi-biennial oscillation(QBO)(2.44 yr);and the solar cycle(11.6 yr),which indicates the critical roles of the QBO and solar activities in the predictability of the NAO.展开更多
In this paper, we investigate a novel technique that reconstructs the observed time series and incorporates driving forces. Furthermore, to illustrate and test the technique, we consider a couple of predictive experim...In this paper, we investigate a novel technique that reconstructs the observed time series and incorporates driving forces. Furthermore, to illustrate and test the technique, we consider a couple of predictive experiments using ideal time series provided by the logistic and Lorenz systems with specific driving forces. The preliminary results show this approach can improve prediction proficiency to some extent, and the external forces play a similar role to that of state variables.展开更多
基金This project is supported by National Natural Science Foundation of China
文摘In this paper the concept of Chaos and its applications to the study of predictability theory is introduced.The author's attempt is to give a general overview of ideas and methods involved in this problem to scientists,who are interested in the problem of predictability but not familiar with the theory of chaos.The problem is discussed in 4 sections.In the first section,the concept of chaos and the study methods are outlined briefly;in the second section,the methods of quantitatively measuring the main characteristics of chaos which are the basis for the predictability theory are introduced;the third section discusses the time series analysis for directly studying chaotic phenomena in practical problems;and the last section presents some research results on the chaotic characteristics and the predictability of the real atmosphere.
基金supported by the National Key R&D Program of China [grant number 2017YFC1501804]the National Natural Science Foundation of China [grant number41575058]
文摘The North Atlantic Oscillation(NAO)is the most prominent mode of atmospheric variability in the Northern Hemisphere.Because of the close relationship between the NAO and regional climate in Eurasia,North Atlantic,and North America,improving the prediction skill for the NAO has attracted much attention.Previous studies that focused on the predictability of the NAO were often based upon simulations by climate models.In this study,the authors took advantage of Slow Feature Analysis to extract information on the driving forces from daily NAO index and introduced it into phase-space reconstruction.By computing the largest Lyapunov exponent,the authors found that the predictability of daily NAO index shows a significant increase when its driving force signal is considered.Furthermore,the authors conducted a short-term prediction for the NAO by using a global prediction model for chaotic time series that incorporated the driving-force information.Results showed that the prediction skill for the NAO can be largely increased.In addition,results from wavelet analysis suggested that the driving-force signal of the NAO is associated with three basic drivers:the annual cycle(1.02 yr),the quasi-biennial oscillation(QBO)(2.44 yr);and the solar cycle(11.6 yr),which indicates the critical roles of the QBO and solar activities in the predictability of the NAO.
基金the National Natural Science Foundation of China (40890052, 41075061 and 40940023)
文摘In this paper, we investigate a novel technique that reconstructs the observed time series and incorporates driving forces. Furthermore, to illustrate and test the technique, we consider a couple of predictive experiments using ideal time series provided by the logistic and Lorenz systems with specific driving forces. The preliminary results show this approach can improve prediction proficiency to some extent, and the external forces play a similar role to that of state variables.
