Some Aspects of Atmospheric Predictability

1984 ◽  
pp. 1-20 ◽  
Author(s):  
E. Lorenz
Keyword(s):  
Atmospheric Predictability

scholarly journals Revisiting the Question of Atmospheric Predictability

2015 ◽  
Vol 7 (2) ◽  
Author(s):  
Isimar De Azevedo Santos ◽  
Julio Buchmann
Keyword(s):  
Atmospheric Predictability

Atmospheric predictability on various scales

1976 ◽  
Vol 63 (11) ◽  
pp. 506-512 ◽  
Author(s):  
A. Wiin-Nielsen
Keyword(s):  
Atmospheric Predictability

CHAOS AND WEATHER FORECASTING: THE ROLE OF THE UNSTABLE SUBSPACE IN PREDICTABILITY AND STATE ESTIMATION PROBLEMS

2011 ◽  
Vol 21 (12) ◽  
pp. 3389-3415 ◽  
Author(s):  
ANNA TREVISAN ◽  
LUIGI PALATELLA
Keyword(s):  
Data Assimilation ◽  
Chaotic Systems ◽  
Weather Forecasting ◽  
Variational Assimilation ◽  
Forecasting Models ◽  
Operational Forecasting ◽  
Estimation Problems ◽  
Atmospheric Predictability ◽  
Unstable Subspace

In the first part of this paper, we review some important results on atmospheric predictability, from the pioneering work of Lorenz to recent results with operational forecasting models. Particular relevance is given to the connection between atmospheric predictability and the theory of Lyapunov exponents and vectors. In the second part, we briefly review the foundations of data assimilation methods and then we discuss recent results regarding the application of the tools typical of chaotic systems theory described in the first part to well established data assimilation algorithms, the Extended Kalman Filter (EKF) and Four Dimensional Variational Assimilation (4DVar). In particular, the Assimilation in the Unstable Space (AUS), specifically developed for application to chaotic systems, is described in detail.

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