Probabilistic temperature forecasting based on an ensemble AR modification

Abstract

To address the uncertainty in outputs of numerical weather prediction (NWP) models, ensembles of forecasts are used. To obtain such an ensemble of forecasts the NWP model is run multiple times, each time with different formulations and/or initial or boundary conditions. To correct for possible biases and dispersion errors in the ensemble, statistical postprocessing models are frequently employed. These statistical models yield full predictive probability distributions for a weather quantity of interest and thus allow for a more accurate assessment of forecast uncertainty. This paper proposes to combine the state of the art Ensemble Model Output Statistics (EMOS) with an ensemble that is adjusted by an AR process fitted to the respective error series by a spread-adjusted linear pool (SLP) in case of temperature forecasts. The basic ensemble modification technique we introduce may be used to simply adjust the ensemble itself as well as to obtain a full predictive distribution for the weather quantity. As demonstrated for temperature forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble, the proposed procedure gives rise to improved results upon the basic (local) EMOS method.

arXiv comment18 pages, 4 figures, 5 tables

References in corpus (8)

• Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling
• Probabilistic Quantitative Precipitation Forecasting Using Ensemble Model Output Statistics
• Multivariate probabilistic forecasting using Bayesian model averaging and copulas
• Ensemble model output statistics for wind vectors
• Spatial postprocessing of ensemble forecasts for temperature using nonhomogeneous Gaussian regression
• Spatially adaptive post-processing of ensemble forecasts for temperature
• Joint probabilistic forecasting of wind speed and temperature using Bayesian model averaging
• Probabilistic wind speed forecasting in Hungary

Cited by in corpus (2)

• Combining predictive distributions for statistical post-processing of ensemble forecasts
• Probabilistic Temperature Forecasting with a Heteroscedastic Autoregressive Ensemble Postprocessing model