Probabilistic Quantitative Precipitation Forecasting Using Ensemble Model Output Statistics

Abstract

Statistical post-processing of dynamical forecast ensembles is an essential component of weather forecasting. In this article, we present a post-processing method that generates full predictive probability distributions for precipitation accumulations based on ensemble model output statistics (EMOS). We model precipitation amounts by a generalized extreme value distribution that is left-censored at zero. This distribution permits modelling precipitation on the original scale without prior transformation of the data. A closed form expression for its continuous rank probability score can be derived and permits computationally efficient model fitting. We discuss an extension of our approach that incorporates further statistics characterizing the spatial variability of precipitation amounts in the vicinity of the location of interest. The proposed EMOS method is applied to daily 18-h forecasts of 6-h accumulated precipitation over Germany in 2011 using the COSMO-DE ensemble prediction system operated by the German Meteorological Service. It yields calibrated and sharp predictive distributions and compares favourably with extended logistic regression and Bayesian model averaging which are state of the art approaches for precipitation post-processing. The incorporation of neighbourhood information further improves predictive performance and turns out to be a useful strategy to account for displacement errors of the dynamical forecasts in a probabilistic forecasting framework.

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 (24)

• Neural networks for post-processing ensemble weather forecasts
• Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling
• Spatial postprocessing of ensemble forecasts for temperature using nonhomogeneous Gaussian regression
• Censored and shifted gamma distribution based EMOS model for probabilistic quantitative precipitation forecasting
• Combining predictive distributions for statistical post-processing of ensemble forecasts
• Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting
• Forest-based methods and ensemble model output statistics for rainfall ensemble forecasting
• Similarity-based semi-local estimation of EMOS models
• Isotonic Distributional Regression
• Joint probabilistic forecasting of wind speed and temperature using Bayesian model averaging
• Probabilistic temperature forecasting based on an ensemble AR modification
• Comparison of multivariate post-processing methods using global ECMWF ensemble forecasts
• Non-parametric Kernel-Based Estimation of Probability Distributions for Precipitation Modeling
• Statistical post-processing of hydrological forecasts using Bayesian model averaging
• Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature
• Truncated generalized extreme value distribution based EMOS model for calibration of wind speed ensemble forecasts
• Combining low-dimensional ensemble postprocessing with reordering methods
• Statistical post-processing of heat index ensemble forecasts: is there a royal road?
• A two-step machine learning approach to statistical post-processing of weather forecasts for power generation
• Machine learning-based probabilistic forecasting of solar irradiance in Chile
• Statistical post-processing of ensemble forecasts of temperature in Santiago de Chile
• Statistical post-processing of dual-resolution ensemble forecasts
• Post-processing multi-ensemble temperature and precipitation forecasts through an Exchangeable Gamma Normal model and its Tobit extension
• Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods