Do probabilistic forecasts lead to better decisions?

Ramos, Maria‐Helena; Andel, Schalk Jan van; Pappenberger, Florian

doi:10.5194/hessd-9-13569-2012

Cited by 38 publications

(49 citation statements)

References 30 publications

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“…This allows decision makers to take more risk when forecasts are estimated to be more certain [109]. Another effect of using uncertainty information is that decisions from several individuals tend to converge, meaning lower variability of test results [110]. This implies that uncertainty information can allow for more systematic decision-making, especially among multiple individuals.…”

Section: Communication Of Weather Uncertaintymentioning

confidence: 99%

“…Several research groups designed experiments to study the behavior and decision quality of decision-makers using forecast uncertainty information (see Section 5.1 for further details). Experiments such as those in hydrological forecasting and flood protection [110] can be borrowed by system operators and market players to increase the awareness of their technical staff on the added value of forecast uncertainty information, and train them to gauge confidence in using this information. An example of good practice is the "Ensemble Prediction System (EPS) Training" (http://collaboration.cmc.ec.gc.ca/cmc/ ensemble/Formation-Training/Read-me.html) course created by the Meteorological Service of Canada.…”

Section: Recommendations and Best Practicesmentioning

confidence: 99%

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Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

Bessa

Möhrlen²,

Fundel

et al. 2017

Energies

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Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding of its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. This paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. A set of recommendations for standardization and improved training of operators are provided along with examples of best practices.

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Section: Communication Of Weather Uncertaintymentioning

confidence: 99%

Section: Recommendations and Best Practicesmentioning

confidence: 99%

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

Bessa

Möhrlen²,

Fundel

et al. 2017

Energies

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“…In this approach, it is expected that the uncertainty related to the meteorological portion of the forecasting system is sampled and thus permits better decision-making (Cloke and Pappenberger 2009;Ramos et al 2013). Although meteorological ensembles do not sample all uncertainties in the forecasting system (for example, uncertainties in the hydrological model are not considered), the meteorological forecast are usually the ones which introduce the highest amount of uncertainty to the system.…”

Section: Introductionmentioning

confidence: 99%

“…Although meteorological ensembles do not sample all uncertainties in the forecasting system (for example, uncertainties in the hydrological model are not considered), the meteorological forecast are usually the ones which introduce the highest amount of uncertainty to the system. This method for generating hydrological ensemble forecasts is actually broadly applied and has been showing better performance than deterministic forecasts in terms of performance metrics (Renner et al 2009;Velázquez et al 2009;Jaun and Ahrens 2009), decision making assessments (Boucher et al 2011;Ramos et al 2013), and also more recently to reservoirs operation assessments (Mccollor and Stull 2008;Zhao et al 2011;Boucher et al 2012;Raso et al 2013). …”

Section: Introductionmentioning

confidence: 99%

Short-Term Reservoir Optimization for Flood Mitigation under Meteorological and Hydrological Forecast Uncertainty

Schwanenberg

Fan

Naumann

et al. 2015

Water Resour Manage

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State-of-the-art applications of short-term reservoir management integrate several advanced components, namely hydrological modelling and data assimilation techniques for predicting streamflow, optimization-based techniques for decision-making on the reservoir operation and the technical framework for integrating these components with data feeds from gauging networks, remote sensing data and meteorological weather predictions. In this paper, we present such a framework for the short-term management of reservoirs operated by the a drainage area of approximately 55,000 km and its operation for flood mitigation. Basis for the anticipatory short-term management of the reservoir over a forecast horizon of up to 15 days are streamflow predictions of the MGB hydrological model. The semi-distributed model is well suited to represent the watershed and shows a Nash-Sutcliffe model performance in the order of 0.83-0.90 for most streamflow gauges of the data-sparse basin. A lead time performance assessment of the deterministic and probabilistic ECMWF forecasts as model forcing indicate the superiority of the probabilistic model. The novel short-term optimization approach consists of the reduction of the ensemble forecasts into scenario trees as an input of a multi-stage stochastic optimization. We show that this approach has several advantages over commonly used deterministic methods which neglect forecast uncertainty in the short-term decision-making. First, the probabilistic forecasts have longer forecast horizons that allow an earlier and therefore better anticipation of critical flood events. Second, the stochastic optimization leads to more robust decisions than deterministic procedures which consider only a single future trajectory. Third, the stochastic optimization permits to introduce advanced chance constraints for refining the system operation.

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“…However, streamflow forecasts are inherently uncertain. While single (or deterministic) forecasts makes end-users over-confident, probabilistic forecasts, which provide an estimate of the uncertainty, enable rational decision making [1,2] and offer additional economic value [3]. Although difficulties still remain in the use of probabilities [4,5], several operational flood forecasting services now routinely run Hydrological Ensemble Prediction Systems (HEPS) which provide probabilistic streamflow forecasts based on a number of possible hydrological scenarios (among others: EFAS [6], NOAA's HEFS [7], see [8] for a review).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic flood forecasting on the Rhone River: evaluation with ensemble and analogue-based precipitation forecasts

Bellier

Zin

Siblot³

et al. 2016

E3S Web Conf.

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Abstract. Hydrological ensemble forecasting performances are analysed over 5 basins up to 2000 km² in the French Upper Rhone region. Streamflow forecasts are issued at an hourly time step from lumped ARX rainfall-runoff models forced by different precipitation forecasts. Ensemble meteorological forecasts from ECMWF and NCEP are considered, as well as analogue-based forecasts fed by their corresponding control forecast. Analogue forecasts are rearranged using an adaptation of the Schaake-Shuffle method in order to ensure the temporal coherence. A new evaluation approach is proposed, separating forecasting performances on peak amplitudes and peak timings for high flow events. Evaluation is conducted against both simulated and observed streamflow (so that relative meteorological and hydrological uncertainties can be assessed), by means of CRPS and rank histograms, over the 2007-2014 period. Results show a general agreement of the forecasting performances when averaged over the 5 basins. However, ensemble-based and analogue-based streamflow forecasts produce a different signature on peak events in terms of bias, spread and reliability. Strengths and weaknesses of both approaches are discussed as well as potential improvements, notably towards their merging.

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Do probabilistic forecasts lead to better decisions?

Cited by 38 publications

References 30 publications

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

Short-Term Reservoir Optimization for Flood Mitigation under Meteorological and Hydrological Forecast Uncertainty

Probabilistic flood forecasting on the Rhone River: evaluation with ensemble and analogue-based precipitation forecasts

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