Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Power law error growth in multi-hierarchical chaotic systems -- a
  dynamical mechanism for finite prediction horizon in weather forecasts   We propose a dynamical mechanism for a scale dependent error growth rate, by
the introduction of a class of hierarchical models. The coupling of time scales
and length scales is motivated by atmospheric dynamics. This model class can be
tuned to exhibit a scale dependent error growth rate in the form of a power
law, which translates in power law error growth over time instead of
exponential error growth as in conventional chaotic systems. The consequence is
a strictly finite prediction horizon, since in the limit of infinitesimal
errors of initial conditions, the error growth rate diverges and hence
additional accuracy is not translated into longer prediction times. By
re-analyzing data of the NCEP Global Forecast System published by Harlim et
al.[13] we show that such a power law error growth rate can indeed be found in
numerical weather forecast models.

28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an