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Algorithmic tuning of spread–skill relationship in ensemble forecasting systems
Author(s) -
Ekblom Madeleine,
Tuppi Lauri,
Shemyakin Vladimir,
Laine Marko,
Ollinaho Pirkka,
Haario Heikki,
Järvinen Heikki
Publication year - 2019
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3695
Subject(s) - ensemble forecasting , computer science , ensemble learning , task (project management) , ensemble average , filter (signal processing) , boundary (topology) , artificial intelligence , machine learning , algorithm , mathematics , engineering , mathematical analysis , systems engineering , climatology , computer vision , geology
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.