Generation of ensemble precipitation forecast from single-valued quantitative precipitation forecast for hydrologic ensemble prediction

Wu, Limin; Seo, Dong Jun; Demargne, Julie; Brown, James Dean; Cong, S.; Schaake, John C.

doi:10.1016/j.jhydrol.2011.01.013

Cited by 118 publications

(81 citation statements)

References 23 publications

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“…However, using rainfall ensembles to make hydrological predictions is not as straightforward and has proven to be very challenging (Wu et al 2011). According to Schaake et al (2007) much remains to be done to make them reliable enough for operational hydrological predictions.…”

Section: Introductionmentioning

confidence: 99%

“…The approach presented here has some similarities to the method described first by Schaake et al (2007), which was then further improved by Wu et al (2011), in the sense that it portions the historical observed and corresponding forecasted data into four sub-regions to estimate the uncertainty of the rainfall forecast and provide a probabilistic hydrological forecast.…”

Section: Introductionmentioning

confidence: 99%

“…Although the approach presented here shares similarities in the estimation methods with the method presented in Wu et al (2011), the application is different as well as some of the assumptions and hence their implications are different.…”

Section: Introductionmentioning

confidence: 99%

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A methodology for probabilistic real-time forecasting – an urban case study

René

Madsen

Mark

2012

Journal of Hydroinformatics

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The phenomenon of urban flooding due to rainfall exceeding the design capacity of drainage systems is a global problem and can have significant economic and social consequences. The complex nature of quantitative precipitation forecasts (QPFs) from numerical weather prediction (NWP) models has facilitated a need to model and manage uncertainty. This paper presents a probabilistic approach for modelling uncertainty from single-valued QPFs at different forecast lead times. The uncertainty models in the form of probability distributions of rainfall forecasts combined with a sewer model is an important advancement in real-time forecasting at the urban scale. The methodological approach utilized in this paper involves a retrospective comparison between historical forecasted rainfall from a NWP model and observed rainfall from rain gauges from which conditional probability distributions of rainfall forecasts are derived. Two different sampling methods, respectively, a direct rainfall quantile approach and the Latin hypercube sampling based method were used to determine the uncertainty in forecasted variables (water level, volume) for a test urban area, the city of Aarhus. The results show the potential for applying probabilistic rainfall forecasts and their subsequent use in urban drainage forecasting for estimation of prediction uncertainty.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A methodology for probabilistic real-time forecasting – an urban case study

René

Madsen

Mark

2012

Journal of Hydroinformatics

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“…These FFS rainfall estimates can be provided by the deterministic QPF originated by limited-area meteorological models, or by extrapolation and the use of trend-based techniques when the forecasting horizon is on the order of six hours or less [1,12]. The use of these techniques is referred to as 'nowcasting' of which an extensive review can be found in [51,52,55].…”

Section: Introductionmentioning

confidence: 99%

Comparison of recorded rainfall with quantitative precipitation forecast in a rainfall-runoff simulation for the Langat River Basin, Malaysia

et al. 2011

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Observed rainfall is used for runoff modeling in flood forecasting where possible, however in cases where the response time of the watershed is too short for flood warning activities, a deterministic quantitative precipitation forecast (QPF) can be used. This is based on a limited-area meteorological model and can provide a forecasting horizon in the order of six hours or less. This study applies the results of a previously developed QPF based on a 1D cloud model using hourly NOAA-AVHRR (Advanced Very High Resolution Radiometer) and GMS (Geostationary Meteorological Satellite) datasets. Rainfall intensity values in the range of 3-12 mm/hr were extracted from these datasets based on the relation between cloud top temperature (CTT), cloud reflectance (CTR) and cloud height (CTH) using defined thresholds. The QPF, prepared for the rainstorm event of 27 September to 8 October 2000 was tested for rainfall runoff on the Langat River Basin, Malaysia, using a suitable NAM rainfall-runoff model. The response of the basin both to the rainfall-runoff simulation using the QPF estimate and the recorded observed rainfall is compared here, based on their corresponding discharge hydrographs. The comparison of the QPF and recorded rainfall showed R 2 = 0.9028 for the entire basin. The runoff hydrograph for the recorded rainfall in the Kajang sub-catchment showed R 2 = 0.9263 between the observed and the simulated, while that of the QPF rainfall was R 2 = 0.819. This similarity in runoff suggests there is a high level of accuracy shown in the improved QPF, and that significant improvement of flood forecasting can be achieved through 'Nowcasting', thus increasing the response time for flood early warnings.

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“…The basic idea is to 5 develop a statistical model by exploiting the joint relationship between observations and NWP forecasts, estimate the model parameters using past data, and reproduce post-processed ensemble forecasts of the future (Roulin and Vannitsem, 2012;Robertson et al, 2013;Chen et al, 2014;Khajehei, 2015;Shrestha et al, 2015;Khajehei and Moradkhani, 2017;Schaake et al, 2007;Wu et al, 2011;Tao et al, 2014). The range of complexity in the post processing approaches vary from regression-based approaches to parametric or non-parametric models based on the meteorological variables (wind speed, 10 temperature, precipitation etc.)…”

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confidence: 99%

Evaluation of ensemble precipitation forecasts generated through postprocessing in a Canadian catchment

Jha¹,

Shrestha²,

Stadnyk³

et al. 2017

Preprint

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Abstract. Flooding in Canada is often caused by heavy rainfall during the snowmelt period. Hydrologic forecast centers rely on precipitation forecasts obtained from numerical weather prediction (NWP) models to enforce hydrological models for 10 streamflow forecasting. The uncertainties in raw quantitative precipitation forecasts (QPFs) are enhanced by physiography and orography effect over diverse landscape, particularly in the western catchments of Canada. A Bayesian post-processing approach called rainfall-post processing (RPP), developed in Australia Shrestha et al., 2015), has been applied to assess its forecast performance in a Canadian catchment. Raw QPFs obtained from two sources, Global ensemble forecasting system (GEFS) Reforecast 2 project from National Centers for Environmental Protection (NCEP), and 15 Global deterministic forecast system (GDPS) from Environment and Climate Change Canada (ECCC) are used in this study.The study period from Jan 2013 to Dec 2015 covered a major flood event in Calgary, Alberta, Canada. Post-processed results show that the RPP is able to remove the bias, and reduce the continuous ranked probability score of both GEFS and GDPS forecasts. Ensembles generated from the RPP better depict the forecast uncertainty.

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Generation of ensemble precipitation forecast from single-valued quantitative precipitation forecast for hydrologic ensemble prediction

Cited by 118 publications

References 23 publications

A methodology for probabilistic real-time forecasting – an urban case study

A methodology for probabilistic real-time forecasting – an urban case study

Comparison of recorded rainfall with quantitative precipitation forecast in a rainfall-runoff simulation for the Langat River Basin, Malaysia

Evaluation of ensemble precipitation forecasts generated through postprocessing in a Canadian catchment

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