8:30 AM-9:15 AM
Chair: Andrew F. B. Tompson, Lawrence Livermore National Laboratory
Atmospheric prediction has historically been an applied science which has provided early examples of mathematically interesting phenomena such as low order chaotic dynamics and sensitive dependence on initial conditions. The nature of the posed forecast problems require, the development of methodologies for dealing with the fact that the atmosphere is a system with a very large number of degrees of freedom and which exhibits sensitive dependence on both initial conditions and forcing. Over the past several years, operational forecasting centers have initiated research in two major areas -- four-dimensional data assimilation, for the purpose of initial state estimation, and coarse grained probabilistic predictions at the limits of deterministic predictability, for extended range outlooks.
The speaker will discuss both problems and the current, state-of-the-art techniques. He will examine the limitations of the currently used methods, which are derived from linear theory, and suggest the need for nonlinear extension. In order to reach a level of utility commensurate with computational expense, probabilistic predictions in the short range (for data assimilation purposes) or the extended range (for climatic anomaly predictions) must be capable of giving probabilistic information for the situation where a probability density forecast becomes multi-modal. A prototypical, simplest example of such a situation is the planetary-wave regime transition which the speaker will examine in detail.
Joseph J. Tribbia
National Center for Atmospheric Research
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