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Ensemble transform Kalman filter perturbations for a regional ensemble prediction system
Author(s) -
Bowler Neill E.,
Mylne Kenneth R.
Publication year - 2009
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.404
Subject(s) - ensemble forecasting , ensemble average , ensemble kalman filter , kalman filter , downscaling , ensemble learning , statistical physics , range (aeronautics) , mathematics , computer science , meteorology , statistics , extended kalman filter , physics , geology , climatology , artificial intelligence , precipitation , materials science , composite material
The Met Office has been routinely running a short‐range ensemble prediction system since the summer of 2005. This system consists of two component ensembles, a global ensemble that provides lateral boundary conditions to a regional ensemble. The global ensemble calculates the initial condition perturbations using a local version of the ensemble transform Kalman filter (ETKF). This article details tests of an ETKF run specifically for the regional ensemble. The perturbations using the ETKF for the regional ensemble contain more detail on small scales and less power on large scales. This extra small‐scale detail persists for less than 18 h. These perturbations are overall smaller than the ones derived from the global ensemble, since they have been explicitly scaled to match the average error of the ensemble mean forecast at T + 6 h. Aside from the difference in the overall scaling of the perturbations, the skill of the two ensembles is very similar, with slightly higher skill being seen when the perturbations are derived from the global ensemble. The ETKF for the regional ensemble was implemented during May 2007. This change has recently been reversed, following these results. These comparisons may shed some light on whether the regional ensemble is providing a ‘dynamical downscaling’ of the global ensemble, or unique information of its own. In these tests the ensemble run with perturbations from the global ensemble was slightly more skilful than the regional perturbations ensemble, indicating that improving on dynamical downscaling is likely to be difficult. ©Crown Copyright 2009. Reproduced with the permission of HMSO. Published by John Wiley & Sons Ltd.