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| Titel |
Adaptive correction of deterministic models to produce accurate probabilistic forecasts |
| VerfasserIn |
Paul Smith, Keith Beven  |
| Konferenz |
EGU General Assembly 2010
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 12 (2010) |
| Datensatznummer |
250035788
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| Zusammenfassung |
| The basis of many operational hydrological forecasting systems are one or more process
based models producing deterministic forecasts. Often significant resources have been
invested in acquiring these models and users have familiar with there use. In many situations
such models produce biased forecasts. Online data assimilation can be used to address this
but many techniques, such as Ensemble Kalman filtering, introduce a significant
computational cost due to multiple calls to the hydrological model. An alternative
methodology for online data assimilation, utilised in the UK National Flood Forecasting
System, is outlined in this work. This methodology uses a stochastic multiplicative gain to
correct the deterministic model predictions at each observation location. The evolution of this
gain is evaluated, at minimal cost, using a linear Kalman filter. The efficiency of this
technique is demonstrated on an example application; the Upper River Severn in the UK. By
considering multiple observation locations covered by a single hydrological model the
robustness of the approach to missing data is demonstrated. The ability to provide
corrections at unobserved locations utilising short term monitoring data is also discussed. |
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