A1 Journal article (refereed), original research

Algorithmic tuning of spread‐skill relationship in ensemble forecasting systems

Open Access publication

Publication Details
Authors: Ekblom Madeleine, Tuppi Lauri, Shemyakin Vladimir, Laine Marko, Ollinaho Pirkka, Haario Heikki, Järvinen Heikki
Publisher: Wiley: 12 months
Publication year: 2019
Language: English
Related Journal or Series Information: Quarterly Journal of the Royal Meteorological Society
Volume number: 146
Issue number: 727
Start page: 598
End page: 612
Number of pages: 15
ISSN: 0035-9009
eISSN: 1477-870X
JUFO-Level of this publication: 2
Open Access: Open Access publication
Location of the parallel saved publication: http://urn.fi/URN:NBN:fi-fe202003188388


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.

Last updated on 2020-23-03 at 13:04