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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.
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 which typifies the members within
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- that cluster.
In the current medium-range ENS forecasts, the clustering summarises Weather scenario clusters summarise the range of synoptic flow patterns (weather scenario clustering) in a restricted area covering Europe and the northwest Atlantic. Each the medium-range ensemble forecasts. Each cluster is represented by the ensemble member closest to its centre, referred to as the Most Representative Member most representative member (MRM or cluster scenario) for that cluster. Each MRM is then attributed to
Weather regime clusters are one of the four climatological regimes (weather regime clustering) which have been derived from reanalysed data.
Current use of clustering
In the current medium range forecasts, weather scenario clustering summarises the range of synoptic flow patterns in a restricted area covering Europe and the northwest Atlantic. Each most representative member is then attributed to one of the weather regime clusters. This shows the differences between scenarios in terms of the large-scale flow and provides information about the possible transitions between regimes during the forecast.
In the current extended-range and seasonal forecasts, the " weather regime " clustering is used from the outset , although but the weather regimes that are used differ from those used with ENS ensemble medium range weather regime clustering.
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