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| Titel |
Characteristics of occasional poor medium-range weather forecasts for Europe |
| VerfasserIn |
Mark Rodwell, Linus Magnusson, Peter Bauer, Peter Bechtold, Massimo Bonavita, Carla Cardinali, Michail Diamantakis, Paul Earnshaw, Antonio Garcia-Mendez, Lars Isaksen, Erland Källén, Daniel Klocke, Philippe Lopez, Tony McNally, Anders Persson, Fernando Prates, Nils Wedi |
| Konferenz |
EGU General Assembly 2013
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 15 (2013) |
| Datensatznummer |
250075784
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| Zusammenfassung |
| Weather prediction at a range of a few days has become more skilful over recent decades, but forecast centres still suffer from occasional very poor forecasts - often referred to as a 'drop-outs' or 'busts'. This study focuses on European Centre for Medium-range Weather Forecasts (ECMWF) day-6 forecasts for Europe. The focus on particularly poor forecasts means that one must be careful what can be concluded for a single case-study, and what requires the use of a large composite of cases.
Although busts are defined by area-mean scores, bust composites reveal a coherent 'Rex-type' blocking situation; with a High over North Europe and a Low over the Mediterranean. Initial conditions for these busts also reveal a coherent flow, but this is located over North America and involves a trough over the Rockies, with high convective instability to its east. This flow-type occurs particularly in spring, and is often associated with a Rossby wave that has crossed the Pacific.
A composite of this initial flow-type does indeed display enhanced day-6 random forecast errors - indicating reduced inherent predictability. In the probabilistic forecasting system, ensemble spread is also enhanced but not sufficiently to match the increased error.
Composite analysis of the Potential Vorticity budget shows that mesoscale convective systems, associated with the convective instability, act to slow the motion of the trough. Hence convection errors play an active role in the busts. The sub-grid-scale nature of convection highlights the importance of the representation of model uncertainty in probabilistic forecasts.
The cloud and extreme conditions associated with mesoscale convective systems also reduce the availability and utility of observations provided to the data assimilation.
A question of relevance to the wider community is do we have observations with sufficient accuracy to better constrain the important error-structures in the initial-conditions? Meanwhile, improvements to ensemble prediction systems - that better estimate flow-dependent errors in the background, observations and model - should help us better predict the increase in forecast uncertainty. |
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