8,968 research outputs found
An early warning indicator for atmospheric blocking events using transfer operators
The existence of persistent midlatitude atmospheric flow regimes with
time-scales larger than 5-10 days and indications of preferred transitions
between them motivates to develop early warning indicators for such regime
transitions. In this paper, we use a hemispheric barotropic model together with
estimates of transfer operators on a reduced phase space to develop an early
warning indicator of the zonal to blocked flow transition in this model. It is
shown that, the spectrum of the transfer operators can be used to study the
slow dynamics of the flow as well as the non-Markovian character of the
reduction. The slowest motions are thereby found to have time scales of three
to six weeks and to be associated with meta-stable regimes (and their
transitions) which can be detected as almost-invariant sets of the transfer
operator. From the energy budget of the model, we are able to explain the
meta-stability of the regimes and the existence of preferred transition paths.
Even though the model is highly simplified, the skill of the early warning
indicator is promising, suggesting that the transfer operator approach can be
used in parallel to an operational deterministic model for stochastic
prediction or to assess forecast uncertainty
How Random is a Coin Toss? Bayesian Inference and the Symbolic Dynamics of Deterministic Chaos
Symbolic dynamics has proven to be an invaluable tool in analyzing the
mechanisms that lead to unpredictability and random behavior in nonlinear
dynamical systems. Surprisingly, a discrete partition of continuous state space
can produce a coarse-grained description of the behavior that accurately
describes the invariant properties of an underlying chaotic attractor. In
particular, measures of the rate of information production--the topological and
metric entropy rates--can be estimated from the outputs of Markov or generating
partitions. Here we develop Bayesian inference for k-th order Markov chains as
a method to finding generating partitions and estimating entropy rates from
finite samples of discretized data produced by coarse-grained dynamical
systems.Comment: 8 pages, 1 figure; http://cse.ucdavis.edu/~cmg/compmech/pubs/hrct.ht
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