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

Institute of Atmospheric Science and Climate ISAC-CNR, via P. Gobetti 101, 40129 Bologna, Italy

and

CNISM – Dipartimento di Ingegneria dell'Innovazione, Università del Salento, Str. Prov. Lecce-Monteroni km 1,200 73100 Lecce, Italy

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https://doi.org/10.1142/S0218127411030635Cited by:31 (Source: Crossref)

Abstract

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.

Keywords:

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