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Clustering
In order to compress the amount of information being produced by the ENS, and to highlight the most predictable parts, Clustering is the process where individual ensemble members that are "similar" according to some measure (or norm) can are be grouped together. This process is known as Clustering. The norm for measuring which members are “similar” can be defined in different ways. Clustering can be performed 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
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- for different parameters
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- for each forecast time
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- 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 forecast 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 an area covering Europe, eastern Canada, and the north Atlantic. This is used beyond day10 of the medium range ensemble forecast output to provides information about the possible transitions between regimes.
- Weather regime clustering summaries are based on one of six climatological regimes which have been derived from reanalysed data over a slightly larger area covering Europe, eastern Canada, the North Pole region and the north Atlantic. This is used with extended range ensemble forecast output and provides information about the possible transitions between regimes.
. Clustering is a compromise between the advantage of condensing information a large amount of ensemble output against the disadvantage of losing possibly important information. A convenient overview of the ENS ensemble members forecast information can be gained from using Cluster Means :
- a cluster mean (the average over all members in a cluster)
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- a most representative member (MRM or cluster scenario) which is the ensemble member closest to its centre
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- and typifies the members within that cluster. Each most representative member is then attributed to one of the
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- weather regime clusters.
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