Clustering
Clustering is the process where individual ensemble members that are "similar" according to some measure (or norm) are be grouped together. This compresses the large amount of information produced by ensemble members and highlights the most predictable parts. Clustering can be performed:
- over different geographical areas
- for different parameters
- for each forecast time
- over different forecast intervals.
- or a combination of the above.
Currently clustering summarises the range of synoptic flow patterns from the ensemble over a restricted area in two forms:
- Weather scenario clustering is performed on medium range ensemble output over a restricted area covering Europe and the northwest Atlantic. It highlights the differences between medium range scenarios in terms of the large-scale flow.
- Weather regime clustering summaries are based on one of four climatological regimes which have been derived from reanalysed data over a rather larger area covering Europe and the north Atlantic. It also provides information about the possible transitions between regimes during the forecast
Clustering is a compromise between the advantage of condensing a large amount of ensemble output against the disadvantage of losing possibly important information. A convenient overview of ensemble members forecast information can be gained from using:
- a cluster mean (the average over all members in a cluster).
- a most representative member (MRM or cluster scenario) which is the ensemble member closest to its centre and typifies the members within that cluster. Each most representative member is then attributed to one of the weather regime clusters.
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