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| Titel |
Representation of model error in a convective-scale ensemble prediction system |
| VerfasserIn |
L. H. Baker, A. C. Rudd, S. Migliorini, R. N. Bannister |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 21, no. 1 ; Nr. 21, no. 1 (2014-01-08), S.19-39 |
| Datensatznummer |
250120872
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| Publikation (Nr.) |
 copernicus.org/npg-21-19-2014.pdf |
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| Zusammenfassung |
In this paper ensembles of forecasts (of up to six hours) are studied from a
convection-permitting model with a representation of model error due to
unresolved processes. The ensemble prediction system (EPS) used is an
experimental convection-permitting version of the UK Met Office's 24-member
Global and Regional Ensemble Prediction System (MOGREPS). The method of
representing model error variability, which perturbs parameters within the
model's parameterisation schemes, has been modified and we investigate the
impact of applying this scheme in different ways. These are: a control
ensemble where all ensemble members have the same parameter values; an
ensemble where the parameters are different between members, but fixed in
time; and ensembles where the parameters are updated randomly every 30 or
60 min. The choice of parameters and their ranges of variability have been
determined from expert opinion and parameter sensitivity tests. A case of
frontal rain over the southern UK has been chosen, which has a multi-banded
rainfall structure.
The consequences of including model error variability in the case studied are
mixed and are summarised as follows. The multiple banding, evident in the
radar, is not captured for any single member. However, the single band is
positioned in some members where a secondary band is present in the radar.
This is found for all ensembles studied. Adding model error variability with
fixed parameters in time does increase the ensemble spread for near-surface
variables like wind and temperature, but can actually decrease the spread of
the rainfall. Perturbing the parameters periodically throughout the forecast
does not further increase the spread and exhibits "jumpiness" in the spread
at times when the parameters are perturbed. Adding model error variability
gives an improvement in forecast skill after the first 2–3 h of the
forecast for near-surface temperature and relative humidity. For
precipitation skill scores, adding model error variability has the effect of
improving the skill in the first 1–2 h of the forecast, but then of
reducing the skill after that. Complementary experiments were performed where
the only difference between members was the set of parameter values (i.e. no
initial condition variability). The resulting spread was found to be
significantly less than the spread from initial condition variability alone. |
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