Algorithmic tuning of spread–skill relationship in ensemble forecasting systems
Shemyakin, Vladimir; Haario, Heikki; Ekblom, Madeleine; Tuppi, Lauri; Laine, Marko; Ollinaho, Pirkka; Järvinen, Heikki (2019-11-05)
Post-print / Final draft
Shemyakin, Vladimir
Haario, Heikki
Ekblom, Madeleine
Tuppi, Lauri
Laine, Marko
Ollinaho, Pirkka
Järvinen, Heikki
05.11.2019
Quarterly Journal of the Royal Meteorological Society
146
727
598-612
Wiley
School of Engineering Science
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© 2019 Royal Meteorological Society
© 2019 Royal Meteorological Society
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe202003188388
https://urn.fi/URN:NBN:fi-fe202003188388
Tiivistelmä
In ensemble weather prediction systems, ensemble spread is generated using uncertainty representations for initial and boundary values as well as for model formulation. The ensuing ensemble spread is thus regulated through what we call ensemble spread parameters. The task is to specify the parameter values such that the ensemble spread corresponds to the prediction skill of the ensemble mean – a prerequisite for a reliable prediction system. In this paper, we present an algorithmic approach suitable for this task consisting of a differential evolution algorithm with filter likelihood providing evidence. The approach is demonstrated using an idealized ensemble prediction system based on the Lorenz–Wilks system. Our results suggest that it might be possible to optimize the spread parameters without manual intervention.
Lähdeviite
Ekblom, M., Tuppi, L., Shemyakin, V., Laine, M., Ollinaho, P., Haario, H., Järvinen, H. (2020). Algorithmic tuning of spread–skill relationship in ensemble forecasting systems. Quarterly Journal of the Royal Meteorological Society, vol. 146, issue 727. pp. 598-612. DOI: 10.1002/qj.3695
Kokoelmat
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