Nonlinear Processes in Geophysics www.nonlin-processes-geophys.net 1023-5809 1607-7946 8 6 2001 10.5194/npg-8-357-2001 http://www.nonlin-processes-geophys.net/8/357/2001/ http://www.nonlin-processes-geophys.net/8/357/2001/npg-8-357-2001.html http://www.nonlin-processes-geophys.net/8/357/2001/npg-8-357-2001.pdf 357 371 0000-00-00 Model error in weather forecasting D. Orrell L. Smith J. Barkmeijer T. N. Palmer Mathematical Institute, University of Oxford, 24-29 St Giles’, Oxford OX1 3LB, UK Present address: Centre for Nonlinear Dynamics, Department of Civil and Environmental Engineering, University College London, Gower Street, London WC1E 6BT, UK Centre for the Analysis of Time Series, Department of Statistics, London School of Economics, Houghton Street, London WC2A 2AE, UK European Centre for Medium Range Weather Forecasts, Shinfield Park, Reading RG2 9AX, UK 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.