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chao-dyn

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preprint1996arXiv

Coupled Map Modeling for Cloud Dynamics

A coupled map model for cloud dynamics is proposed, which consists of the successive operations of the physical processes; buoyancy, diffusion, viscosity, adiabatic expansion, fall of a droplet by gravity, descent flow dragged by the falling droplet, and advection. Through extensive simulations, the phases corresponding to stratus, cumulus, stratocumulus and cumulonimbus are found, with the change of the ground temperature and the moisture of the air. They are characterized by order parameters such as the cluster number, perimeter-to-area ratio of a cloud, and Kolmogorov-Sinai entropy.

preprint2000arXiv

Computational Mechanics: Pattern and Prediction, Structure and Simplicity

Computational mechanics, an approach to structural complexity, defines a process's causal states and gives a procedure for finding them. We show that the causal-state representation--an $ε$-machine--is the minimal one consistent with accurate prediction. We establish several results on $ε$-machine optimality and uniqueness and on how $ε$-machines compare to alternative representations. Further results relate measures of randomness and structural complexity obtained from $ε$-machines to those from ergodic and information theories.

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