Skill of Medium-Range Hydrological Ensemble Predictions

Roulin, Emmanuel; Vannitsem, Stéphane

doi:10.1175/jhm436.1

Cited by 91 publications

(72 citation statements)

References 20 publications

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“…Conditioning of the ESP upon the El Niño-Southern Oscillation state by schemes involving a weighting of historical climate traces has more recently been explored for operational water supply forecasting at seasonal time scales (e.g., Werner et al, 2004). Additionally, operational weather forecast ensembles such as those distributed by the European Centre for Medium-Range Weather Forecasts (ECMWF) have recently been applied to flood forecasting in research mode (e. g., Gouweleeuw et al, 2005;Roulin and Vannitsem, 2005). Super-ensembles or grand-ensembles, derived from the combination of ensembles from each of several forecast centres, capture NWP uncertainty arising from various sources of model error in addition to initial condition error (Pappenberger et al, 2008).…”

Section: Framework For the Anticipation Of Forecast Uncertaintymentioning

confidence: 99%

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

2012

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This article examines the current practice of streamflow modelling, a field under development for over a century. A sample of the wide range of assessment and planning applications of streamflow models is presented. The diversity in the use of these models is mirrored in the diversity of model complexity, and modelling approaches ranging from empirical to physically based and from lumped to fully distributed are described with examples. Predictions derived from hydrological models are subject to many sources of error; these are discussed along with methods for error minimization or anticipation. Model error is generally quantified using an ensemble of forecasts meant to sample the range of predictive uncertainty. This ensemble can be used to generate reliable probabilistic forecasts of hydrological quantities if all sources of error are accounted for. To date, applications of ensemble methods in streamflow forecasting have typically focused on only one or two error sources. A challenge will be to develop ensemble streamflow forecasts that sample a wider range of predictive uncertainty.

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Section: Framework For the Anticipation Of Forecast Uncertaintymentioning

confidence: 99%

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

2012

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“…Since an important fraction of the uncertainty of hydrological predictions is due to the uncertainty of the input rainfall observations and forecasts, radar-based ensemble nowcasting systems are increasingly used as inputs for flood and sewer system modeling (e.g., Ehret et al, 2008;Silvestro and Rebora, 2012;Silvestro et al, 2013;Xuan et al, 2009Xuan et al, , 2014. At longer forecast ranges, the NWP ensembles are also exploited for uncertainty propagation into hydrological models (see Roulin and Vannitsem, 2005;Thielen et al, 2009;Schellekens et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium

Foresti

Reyniers

Seed

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. The Short-Term Ensemble Prediction System (STEPS) is implemented in real-time at the Royal Meteorological Institute (RMI) of Belgium. The main idea behind STEPS is to quantify the forecast uncertainty by adding stochastic perturbations to the deterministic Lagrangian extrapolation of radar images. The stochastic perturbations are designed to account for the unpredictable precipitation growth and decay processes and to reproduce the dynamic scaling of precipitation fields, i.e., the observation that largescale rainfall structures are more persistent and predictable than small-scale convective cells. This paper presents the development, adaptation and verification of the STEPS system for Belgium (STEPS-BE). STEPS-BE provides in realtime 20-member ensemble precipitation nowcasts at 1 km and 5 min resolutions up to 2 h lead time using a 4 C-band radar composite as input. In the context of the PLURISK project, STEPS forecasts were generated to be used as input in sewer system hydraulic models for nowcasting urban inundations in the cities of Ghent and Leuven. Comprehensive forecast verification was performed in order to detect systematic biases over the given urban areas and to analyze the reliability of probabilistic forecasts for a set of case studies in 2013 and 2014. The forecast biases over the cities of Leuven and Ghent were found to be small, which is encouraging for future integration of STEPS nowcasts into the hydraulic models. Probabilistic forecasts of exceeding 0.5 mm h −1 are reliable up to 60-90 min lead time, while the ones of exceeding 5.0 mm h −1 are only reliable up to 30 min. The STEPS ensembles are slightly under-dispersive and represent only 75-90 % of the forecast errors.

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“…A system based on the ECMWF EPS and the SCHEME hydrological model has been evaluated for two test basins in Belgium using the Brier Skill Score (Roulin and Vannitsem, 2005) and the Relative Economic Value (Roulin, 2007, and presented at the join COST-731 -NetFAM Workshop, in Vilnius, Lithuania). This system was made operational and then extended to the basins of the Meuse and Scheldt in Belgium and France (Van den Bergh and ).…”

Section: Ensemble Nwp For Medium-range Hydrological Forecastsmentioning

confidence: 99%

Propagation of uncertainty from observing systems and NWP into hydrological models: COST‐731 Working Group 2

Zappa

Beven

Bruen³

et al. 2010

Atmospheric Science Letters

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The COST-731 action is focused on uncertainty propagation in hydrometeorological forecasting chains. Goals and activities of the action Working Group 2 are presented. Five foci for discussion and research have been identified: (1) understand uncertainties, (2) exploring, designing and comparing methodologies for the use of uncertainty in hydrological models, (3) providing feedback on sensitivity to data and forecast providers, (4) transferring methodologies among the different communities involved and (5) setting up test-beds and perform proof-of-concepts. Current examples of different perspectives on uncertainty propagation are presented.

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Skill of Medium-Range Hydrological Ensemble Predictions

Cited by 91 publications

References 20 publications

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium

Propagation of uncertainty from observing systems and NWP into hydrological models: COST‐731 Working Group 2

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