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| Titel |
Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting |
| VerfasserIn |
D. E. Robertson, D. L. Shrestha, Q. J. Wang |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 17, no. 9 ; Nr. 17, no. 9 (2013-09-27), S.3587-3603 |
| Datensatznummer |
250085934
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| Publikation (Nr.) |
 copernicus.org/hess-17-3587-2013.pdf |
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| Zusammenfassung |
Sub-daily ensemble rainfall forecasts that
are bias free and reliably quantify forecast uncertainty are critical for
flood and short-term ensemble streamflow forecasting. Post-processing of
rainfall predictions from numerical weather prediction models is typically
required to provide rainfall forecasts with these properties. In this paper,
a new approach to generate ensemble rainfall forecasts by post-processing raw
numerical weather prediction (NWP) rainfall predictions is introduced. The
approach uses a simplified version of the Bayesian joint probability
modelling approach to produce forecast probability distributions for
individual locations and forecast lead times. Ensemble forecasts with
appropriate spatial and temporal correlations are then generated by linking
samples from the forecast probability distributions using the Schaake
shuffle.
The new approach is evaluated by applying it to post-process predictions
from the ACCESS-R numerical weather prediction model at rain gauge locations
in the Ovens catchment in southern Australia. The joint distribution of NWP
predicted and observed rainfall is shown to be well described by the assumed
log-sinh transformed bivariate normal distribution. Ensemble forecasts
produced using the approach are shown to be more skilful than the raw NWP
predictions both for individual forecast lead times and for cumulative
totals throughout all forecast lead times. Skill increases result from the
correction of not only the mean bias, but also biases conditional on the
magnitude of the NWP rainfall prediction. The post-processed forecast
ensembles are demonstrated to successfully discriminate between events and
non-events for both small and large rainfall occurrences, and reliably
quantify the forecast uncertainty.
Future work will assess the efficacy of the post-processing method for a
wider range of climatic conditions and also investigate the benefits of using
post-processed rainfall forecasts for flood and short-term streamflow
forecasting. |
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