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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 6
Nonlin. Processes Geophys., 8, 357–371, 2001
https://doi.org/10.5194/npg-8-357-2001
© Author(s) 2001. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

Special issue: Quantifying Predictability

Nonlin. Processes Geophys., 8, 357–371, 2001
https://doi.org/10.5194/npg-8-357-2001
© Author(s) 2001. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  31 Dec 2001

31 Dec 2001

Model error in weather forecasting

D. Orrell1,2, L. Smith1,3, J. Barkmeijer4, and T. N. Palmer4 D. Orrell et al.
  • 1Mathematical Institute, University of Oxford, 24-29 St Giles’, Oxford OX1 3LB, UK
  • 2Present address: Centre for Nonlinear Dynamics, Department of Civil and Environmental Engineering, University College London, Gower Street, London WC1E 6BT, UK
  • 3Centre for the Analysis of Time Series, Department of Statistics, London School of Economics, Houghton Street, London WC2A 2AE, UK
  • 4European Centre for Medium Range Weather Forecasts, Shinfield Park, Reading RG2 9AX, UK

Abstract. Operational forecasting is hampered both by the rapid divergence of nearby initial conditions and by error in the underlying model. Interest in chaos has fuelled much work on the first of these two issues; this paper focuses on the second. A new approach to quantifying state-dependent model error, the local model drift, is derived and deployed both in examples and in operational numerical weather prediction models. A simple law is derived to relate model error to likely shadowing performance (how long the model can stay close to the observations). Imperfect model experiments are used to contrast the performance of truncated models relative to a high resolution run, and the operational model relative to the analysis. In both cases the component of forecast error due to state-dependent model error tends to grow as the square-root of forecast time, and provides a major source of error out to three days. These initial results suggest that model error plays a major role and calls for further research in quantifying both the local model drift and expected shadowing times.

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