Probabilistic wind speed forecasting on a grid based on ensemble model output statistics

Scheuerer, Michael; Moller, David Wendell

doi:10.1214/15-aoas843

Cited by 73 publications

(71 citation statements)

References 36 publications

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“…In connected works, Kleiber et al . (), Scheuerer and Büermann () and Scheuerer and Möller () considered alternative approaches incorporating techniques from geostatistics and novel model formulations that entail local adaptivity of the parameters and allow for extrapolating the forecasts to locations or grid points without observations. These schemes are particularly important for interpolating local forecasts that are obtained at observation stations to the model grid.…”

Section: Discussionmentioning

confidence: 99%

“…Alternative choices of similarity measures proposed in related works may lead to further improvements for different ensemble prediction systems. For example, Schefzik () proposed a similarity measure defined in terms of mean and variance of the ensemble forecasts, and Kleiber et al . () included covariates such as elevation and land use information.…”

Section: Discussionmentioning

confidence: 99%

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Similarity-Based Semilocal Estimation of Post-Processing Models

Lerch

Baran

2016

Journal of the Royal Statistical Society Series C: Applied Statistics

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Weather forecasts are typically given in the form of forecast ensembles obtained from multiple runs of numerical weather prediction models with varying initial conditions and physics parameterizations. Such ensemble predictions tend to be biased and underdispersive and thus require statistical post-processing. In the ensemble model output statistics approach, a probabilistic forecast is given by a single parametric distribution with parameters depending on the ensemble members.The paper proposes two semilocal methods for estimating the ensemble model output statistics coefficients where the training data for a specific observation station are augmented with corresponding forecast cases from stations with similar characteristics. Similarities between stations are determined by using either distance functions or clustering based on various features of the climatology, forecast errors and locations of the observation stations. In a case-study on wind speed over Europe with forecasts from the 'Grand limited area model ensemble prediction system', the similarity-based semilocal models proposed show significant improvement in predictive performance compared with standard regional and local estimation methods.They further allow for estimating complex models without numerical stability issues and are computationally more efficient than local parameter estimation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Similarity-Based Semilocal Estimation of Post-Processing Models

Lerch

Baran

2016

Journal of the Royal Statistical Society Series C: Applied Statistics

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“…During recent years, efforts have focused on improving probabilistic techniques in order to consider forecast uncertainties [Thorarinsdottir and Gneiting, 2010;Messner et al, 2014;Scheuerer and Möller, 2015]. Probabilistic forecasting can be achieved by considering initial values, boundary conditions, and/or model-related uncertainty [Toth, 2001].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty quantification and predictability of wind speed over the Iberian Peninsula

et al. 2017

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During recent decades, the use of probabilistic forecasting methods has increased markedly. However, these predictions still need improvement in uncertainty quantification and predictability analysis. For this reason, the main aim of this paper is to develop tools for quantifying uncertainty and predictability of wind speed over the Iberian Peninsula. To achieve this goal, several spread indexes extracted from an ensemble prediction system are defined in this paper. Subsequently, these indexes were evaluated with the aim of selecting the most appropriate for the characterization of uncertainty associated to the forecasting. Selection is based on comparison of the average magnitude of ensemble spread (ES) and mean absolute percentage error (MAPE). MAPE is estimated by comparing the ensemble mean with wind speed values from different databases. Later, correlation between MAPE and ES was evaluated. Furthermore, probability distribution functions (PDFs) of spread indexes are analyzed to select the index with greater similarity to MAPE PDFs. Then, the spread index selected as optimal is used to carry out a spatiotemporal analysis of model uncertainty in wind forecasting. The results indicate that mountainous regions and the Mediterranean coast are characterized by strong uncertainty, and the spread increases more rapidly in areas affected by strong winds. Finally, a predictability index is proposed for obtaining a tool capable of providing information on whether the predictability is higher or lower than average. The applications developed may be useful in the forecasting of wind potential several days in advance, with substantial importance for estimating wind energy production.

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“…The work in [69] add a geostatistical step to a non-homogeneous Gaussian regression analysis in order to deliver calibrated wind forecasts at any desired location.…”

Section: Application-specific Weather Forecast Productsmentioning

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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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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Probabilistic wind speed forecasting on a grid based on ensemble model output statistics

Cited by 73 publications

References 36 publications

Similarity-Based Semilocal Estimation of Post-Processing Models

Similarity-Based Semilocal Estimation of Post-Processing Models

Uncertainty quantification and predictability of wind speed over the Iberian Peninsula

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

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