Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation

  title={Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation},
  author={Tilmann Gneiting and Adrian E. Raftery and Anton H Westveld and Tom Goldman},
  journal={Monthly Weather Review},
Abstract Ensemble prediction systems typically show positive spread-error correlation, but they are subject to forecast bias and dispersion errors, and are therefore uncalibrated. This work proposes the use of ensemble model output statistics (EMOS), an easy-to-implement postprocessing technique that addresses both forecast bias and underdispersion and takes into account the spread-skill relationship. The technique is based on multiple linear regression and is akin to the superensemble approach… 

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  • M. ZamoL. BelO. Mestre
  • Environmental Science
    Journal of the Royal Statistical Society: Series C (Applied Statistics)
  • 2020
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