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Stochastic Ensemble Climate Forecast with an Analogue Mode

Preprint published in 2018 by Pascal Yiou, Céline Déandréis
This paper is available in a repository.
This paper is available in a repository.

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Preprint: policy unknown
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Postprint: policy unknown
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This paper presents a system to perform large ensembles climate stochastic forecasts. The system is based on random analogue sampling of sea-level pressure data from the NCEP reanalysis. It is tested to forecast an NAO index and the daily average temperature in five European stations. We simulated 100 member ensembles of averages over lead times from 5 days to 80 days in a hindcast mode, i.e. from a meteorological to a seasonal forecast. We tested the hindcast simulations with usual forecast skill scores (CRPSS or correlation), against persistence and climatology. We find significantly positive skill scores for all time scales. Although this model cannot outperform numerical weather prediction, it presents an interesting benchmark that could complement climatology or persistence forecast.

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