Moment based methods for ensemble assessment and calibration

Abstract

We describe various moment-based ensemble interpretation models for the construction of probabilistic temperature forecasts from ensembles. We apply the methods to one year of medium range ensemble forecasts and perform in and out of sample testing. Our main conclusion is that probabilistic forecasts derived from the ensemble mean using regression are just as good as those based on the ensemble mean and the ensemble spread using a more complex calibration algorithm. The explanation for this seems to be that the predictable component of the variability of the forecast uncertainty is only a small fraction of the total forecast uncertainty. Users of ensemble temperature forecasts are advised, until further evidence becomes available, to ignore the ensemble spread and build probabilistic forecasts based on the ensemble mean alone.

References in corpus (3)

• Use of the likelihood for measuring the skill of probabilistic forecasts
• The Assessment and Calibration of Ensemble Seasonal Forecasts of Equatorial Pacific Ocean Temperature and the Predictability of Uncertainty
• A note on the use of the word 'likelihood' in statistics and meteorology

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