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Original Research Papers

Ensemble prediction for nowcasting with a convection-permitting model – II: forecast error statistics

Authors:

R. N. Bannister ,

National Centre for Earth Observation, Department of Meteorology, University of Reading, Earley Gate, Reading, RG6 6BB, GB
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S. Migliorini,

National Centre for Earth Observation, Department of Meteorology, University of Reading, Earley Gate, Reading, RG6 6BB, GB
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M. A. G. Dixon

Met Office Joint Centre for Mesoscale Meteorology, Department of Meteorology, University of Reading, Earley Gate, Reading, RG6 6BB, GB
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Abstract

A 24-member ensemble of 1-h high-resolution forecasts over the Southern United Kingdom is used to study shortrange forecast error statistics. The initial conditions are found from perturbations from an ensemble transform Kalman filter. Forecasts from this system are assumed to lie within the bounds of forecast error of an operational forecast system. Although noisy, this system is capable of producing physically reasonable statistics which are analysed and compared to statistics implied from a variational assimilation system. The variances for temperature errors for instance show structures that reflect convective activity. Some variables, notably potential temperature and specific humidity perturbations, have autocorrelation functions that deviate from 3-D isotropy at the convective-scale (horizontal scales less than 10 km). Other variables, notably the velocity potential for horizontal divergence perturbations, maintain 3-D isotropy at all scales. Geostrophic and hydrostatic balances are studied by examining correlations between terms in the divergence and vertical momentum equations respectively. Both balances are found to decay as the horizontal scale decreases. It is estimated that geostrophic balance becomes less important at scales smaller than 75 km, and hydrostatic balance becomes less important at scales smaller than 35 km, although more work is required to validate these findings. The implications of these results for high-resolution data assimilation are discussed.

How to Cite: Bannister, R.N., Migliorini, S. and Dixon, M.A.G., 2011. Ensemble prediction for nowcasting with a convection-permitting model – II: forecast error statistics. Tellus A: Dynamic Meteorology and Oceanography, 63(3), pp.497–512. DOI: http://doi.org/10.1111/j.1600-0870.2010.00500.x
  Published on 01 Jan 2011
 Accepted on 29 Dec 2010            Submitted on 14 Apr 2010

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