Comparison of nonhomogeneous regression models for probabilistic wind speed forecasting

Abstract

In weather forecasting, nonhomogeneous regression is used to statistically postprocess forecast ensembles in order to obtain calibrated predictive distributions. For wind speed forecasts, the regression model is given by a truncated normal distribution where location and spread are derived from the ensemble. This paper proposes two alternative approaches which utilize the generalized extreme value (GEV) distribution. A direct alternative to the truncated normal regression is to apply a predictive distribution from the GEV family, while a regime switching approach based on the median of the forecast ensemble incorporates both distributions. In a case study on daily maximum wind speed over Germany with the forecast ensemble from the European Centre for Medium-Range Weather Forecasts, all three approaches provide calibrated and sharp predictive distributions with the regime switching approach showing the highest skill in the upper tail.

References in corpus (3)

• Forecast verification for extreme value distributions with an application to probabilistic peak wind prediction
• Ensemble model output statistics for wind vectors
• Probabilistic quantitative precipitation field forecasting using a two-stage spatial model

Cited by in corpus (25)

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• Similarity-based semi-local estimation of EMOS models
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• Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison
• Statistical post-processing of hydrological forecasts using Bayesian model averaging
• Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature
• Postprocessing of Ensemble Weather Forecasts Using Permutation-invariant Neural Networks
• Bivariate Gaussian models for wind vectors in a distributional regression framework
• Combining low-dimensional ensemble postprocessing with reordering methods
• Truncated generalized extreme value distribution based EMOS model for calibration of wind speed ensemble forecasts
• Statistical post-processing of heat index ensemble forecasts: is there a royal road?
• Proper Scoring Rules for Multivariate Probabilistic Forecasts based on Aggregation and Transformation
• Calibration of wind speed ensemble forecasts for power generation
• Statistical post-processing of dual-resolution ensemble forecasts
• Improving probabilistic forecasts of extreme wind speeds by training statistical post-processing models with weighted scoring rules
• Clustering-based spatial interpolation of parametric post-processing models
• Relaxed Gaussian process interpolation: a goal-oriented approach to Bayesian optimization
• Stochastic simulation of predictive space-time scenarios of wind speed using observations and physical models