Probabilistic quantitative precipitation forecasting using Ensemble Model Output Statistics

By Michael Scheuerer. 2013

194 citations recorded by the source corpus.

Statistical post‐processing of dynamical forecast ensembles is an essential component of weather forecasting. In this article, we present a post‐processing method which generates full predictive probability distributions for precipitation accumulations based on ensemble model output statistics (EMOS).

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/a2e8857e66bbeae6baf7764cb420e78442384b67

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

Statistical Postprocessing of Ensemble Precipitation Forecasts by Fitting Censored, Shifted Gamma Distributions* (2015, unknown): “This will eventually lead to an approach similar to the one proposed by Scheuerer (2014), but based on censored, shifted gamma distributions (CSGD), and a more sophisticated heteroscedastic regression model that accounts for some further peculiarities of precipitation.”
Estimation Methods for Nonhomogeneous Regression Models: Minimum Continuous Ranked Probability Score versus Maximum Likelihood (2018, methodology): “NGR has also been extended from temperature to other meteorological quantities by assuming appropriate forecast distributions (Gneiting et al. 2005; Thorarinsdottir and Gneiting 2010; Messner et al. 2014a,b; Scheuerer 2014; Hemri et al. 2016).”
Evaluating Probabilistic Forecasts with scoringRules (2017, methodology): “For details and examples on choosing appropriate weights, see Scheuerer and Hamill (2015b). In the following, we give a usage example of the multivariate scoring rules using the results from the economic case study in Section 4.”
Spatial Postprocessing of Ensemble Forecasts for Temperature Using Nonhomogeneous Gaussian Regression (2014, methodology): “, bM in (2) are estimated by weighted least squares using a penalized version of the loss function to prevent overfitting [see Scheuerer and König (2014) for details].”
Heteroscedastic Extended Logistic Regression for Postprocessing of Ensemble Guidance (2014, methodology): “The extended logistic regression method has been used in several studies for probabilistic precipitation forecasts (Schmeits and Kok 2010; Ruiz and Saulo 2012; Roulin and Vannitsem 2012; Hamill 2012; Ben Bouall egue 2013; Scheuerer 2013) and was shown to perform very well compared to standard logist”
Extending Extended Logistic Regression: Extended versus Separate versus Ordered versus Censored (2014, methodology): “…logistic regression has been used frequently (Schmeits and Kok 2010; Ruiz and Saulo 2012; Roulin andVannitsem 2012; Hamill 2012; Ben Bouallègue 2013; Scheuerer 2014; Messner et al. 2014) and has been further extended to additionally account for conditional heteroscedasticy (Messner et al. 2014).”
Comparison of non-homogeneous regression models for probabilistic wind speed forecasting (2013, methodology): “In an application to quantitative precipitation, Scheuerer (2012) considers the GEV to be left-censored at zero assigning all mass below zero to exactly zero.”
Neural networks for post-processing ensemble weather forecasts (2018, background): “…has been extended from temperature to other weather variables including wind speed (Thorarinsdottir and Gneiting, 2010; Lerch and Tho-rarinsdottir, 2013; Baran and Lerch, 2015; Scheuerer and M¨oller, 2015) and precipitation (Mess-ner et al., 2014; Scheuerer, 2014; Scheuerer and Hamill, 2015).”