Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting

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 foreca...

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Vydané v:Hydrology and earth system sciences Ročník 17; číslo 9; s. 3587 - 3603
Hlavní autori: Robertson, D. E., Shrestha, D. L., Wang, Q. J.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Katlenburg-Lindau Copernicus GmbH 27.09.2013
Copernicus Publications
Predmet:
Climatic conditions
Climatology
Flood forecasting
Floods
Forecasting
Lead time
Mathematical models
Numerical weather prediction
Post-processing
Prediction models
Probability distributions
Rain
Rain gauges
Rainfall
Stream discharge
Stream flow
Streamflow
Streamflow forecasting
Weather
Weather forecasting
ISSN:1607-7938, 1027-5606, 1607-7938
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Shrnutí: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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ISSN:1607-7938
1027-5606
1607-7938
DOI:10.5194/hess-17-3587-2013