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<front>
<journal-meta>
<journal-id journal-id-type="publisher">NPG</journal-id>
<journal-title-group>
<journal-title>Nonlinear Processes in Geophysics</journal-title>
<abbrev-journal-title abbrev-type="publisher">NPG</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Nonlin. Processes Geophys.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">1607-7946</issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/npg-13-167-2006</article-id>
<title-group>
<article-title>Growth of finite errors in ensemble prediction</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Harle</surname>
<given-names>M.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Kwasniok</surname>
<given-names>F.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Feudel</surname>
<given-names>U.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Institute for Chemistry and Biology of the Marine Environment, University of Oldenburg, Oldenburg,  Germany</addr-line>
</aff>
<pub-date pub-type="epub">
<day>12</day>
<month>06</month>
<year>2006</year>
</pub-date>
<volume>13</volume>
<issue>2</issue>
<fpage>167</fpage>
<lpage>176</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2006 M. Harle et al.</copyright-statement>
<copyright-year>2006</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Generic License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by-nc-sa/2.5/">https://creativecommons.org/licenses/by-nc-sa/2.5/</ext-link></license-p>
</license>
</permissions>
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<self-uri xlink:href="https://npg.copernicus.org/articles/13/167/2006/npg-13-167-2006.pdf">The full text article is available as a PDF file from https://npg.copernicus.org/articles/13/167/2006/npg-13-167-2006.pdf</self-uri>
<abstract>
<p>We study the predictability of chaotic conservative and dissipative
maps in the context of ensemble prediction. Finite-size perturbations around a
reference trajectory are evolved under the full nonlinear system dynamics; this
evolution is characterized by error growth factors and investigated as a
function of prediction time and initial perturbation size. The distribution of
perturbation growth is studied. We then focus on the worst-case predictability,
i.e., the maximum error growth over all initial conditions. The estimate of the
worst-case predictability obtained from the ensemble approach is compared to the
estimate given by the largest singular value of the linearized system dynamics.
For small prediction times, the worst-case error growth obtained from the
nonlinear ensemble approach is exponential with prediction time; for large
prediction times, a power-law dependence is observed the scaling exponent of
which depends systematically on the initial error size.
The question is addressed of how large an ensemble is necessary to reliably
estimate the maximum error growth factor. A power-law dependence of the error in
the estimate of the growth factor on the ensemble size is established empirically.
Our results are valid for several markedly different chaotic conservative and
dissipative systems, perhaps pointing to quite general features.</p>
</abstract>
<counts><page-count count="10"/></counts>
</article-meta>
</front>
<body/>
<back>
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</article>