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Convex error growth patterns in a global weather model.

John Harlim1, Michael Oczkowski, James A Yorke

  • 1Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, USA. jharlim@math.umd.edu

Physical Review Letters
|August 11, 2005
PubMed
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Weather model error growth shows two distinct log-linear phases before saturation. Analysis reveals a convex curve, differing from other saturation dynamics. Keywords: weather model, error growth, saturation value, log-linear regimes.

Area of Science:

  • Meteorology
  • Atmospheric Science
  • Computational Science

Background:

  • Weather models simulate atmospheric dynamics.
  • Understanding error growth is crucial for forecast accuracy.
  • Solutions typically converge to a saturation value.

Purpose of the Study:

  • Investigate the distinct phases of error growth in weather models.
  • Characterize the behavior of error saturation.
  • Compare weather model error dynamics to other systems.

Main Methods:

  • Analysis of the distance E between two typical model solutions.
  • Identification of log-linear regimes for error growth.
  • Graphical analysis of d log(E)/dt versus log(E).

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Main Results:

  • Two distinct log-linear regimes were identified for error growth (E).
  • Regimes exist for E below and above 2% of the saturation value E(s).
  • The plot of d log(E)/dt vs log(E) exhibits a convex shape.

Conclusions:

  • Weather model error growth dynamics are characterized by two log-linear phases.
  • The convex graphical representation distinguishes this behavior from other saturation dynamics.
  • This finding offers new insights into weather model predictability.