Clustering and selection of boundary conditions for limited area ensemble prediction

Bouttier, François ; Raynaud, Laure

Année de publication
2018
Résumé
Limited area ensemble predictions can be sensitive to the specification of lateral boundary conditions, that are often built by subsampling larger ensembles. Using the operational PEARP and AROME-EPS ensembles, we compare several subsampling methods, including random selection, 'representative members' as defined in Molteni (2001), and a new selection method. The tests show that the algorithms used for the clustering and the member selection have a significant impact on the resulting ensembles. Clustering-based methods are shown to outperform random subsampling, mostly (but not only) because they change the ensemble spread. Cluster sizes can be highly variable, which can complicate ensemble interpretation. We present a subsampling algorithm that has little impact on performance scores, but better preserves ensemble spread and produces nearly equally likely members, by limiting cluster size variability.
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