Abstract
Abstract. Nonhomogeneous post-processing is often used to improve the predictive performance of probabilistic ensemble forecasts. A common quantity used to develop, test, and demonstrate new methods is the near-surface air temperature, which is frequently assumed to follow a Gaussian response distribution. However, Gaussian regression models with only a few covariates are often not able to account for site-specific local features leading to uncalibrated forecasts and skewed residuals. This residual skewness remains even if many covariates are incorporated. Therefore, a simple refinement of the classical nonhomogeneous Gaussian regression model is proposed to overcome this problem by assuming a skewed response distribution to account for possible skewness. This study shows a comprehensive analysis of the performance of nonhomogeneous post-processing for the 2 m temperature for three different site types, comparing Gaussian, logistic, and skewed logistic response distributions. The logistic and skewed logistic distributions show satisfying results, in particular for sharpness, but also in terms of the calibration of the probabilistic predictions.
Highlights
Probabilistic weather forecasts have become state-of-the-art in recent years (Gneiting and Katzfuss, 2014)
The expected uncertainty is typically provided by an ensemble prediction system (EPS; Leith, 1974) where multiple forecasts are produced by a numerical weather prediction (NWP) model with slightly perturbed initial conditions, model physics, and parameterizations
Statistical post-processing techniques (Gneiting and Katzfuss, 2014), such as Gaussian ensemble dressing (GED; Roulston and Smith, 2003), nonhomogeneous Gaussian regression (NGR or EMOS; Gneiting et al, 2005), a nonhomogeneous mixture model approach with similarities to Bayesian model averaging (BMA; Raftery et al, 2005), or logistic regression (Wilks, 2009; Messner et al, 2014), are one possibility to correct for these errors
Summary
Probabilistic weather forecasts have become state-of-the-art in recent years (Gneiting and Katzfuss, 2014). Statistical post-processing techniques (Gneiting and Katzfuss, 2014), such as Gaussian ensemble dressing (GED; Roulston and Smith, 2003), nonhomogeneous Gaussian regression (NGR or EMOS; Gneiting et al, 2005), a nonhomogeneous mixture model approach with similarities to Bayesian model averaging (BMA; Raftery et al, 2005), or logistic regression (Wilks, 2009; Messner et al, 2014), are one possibility to correct for these errors. These methods have been extensively tested for air temperature forecasts and other quantities, with NGR (with various extensions) representing one of the most popular approaches
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