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The emphasis is on large-scale developments when clustering ensemble members and so the 500hPa and 1000hPa geopotential forecast fields are used for daily weather scenarios. The area considered covers Europe and its immediate surroundings including the northeast Atlantic.
Fig813.AFig8.1.3.1-1: Area considered for clustering purposes.
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To ensure synoptic consistency, each individual ensemble member must belong to the same cluster throughout the lead-time window. For two members to be assigned to the same cluster, they must display similar synoptic 500hPa development over the whole time window. However, weak gradients in 500hPa forecasts can lead to synoptically rather different members being assigned to the same cluster because of the the RMS norm. The clustering scheme is designed to create no more than six clusters.
Most representative member
A cluster is represented not by the mean of its members but by its most representative member (MRM or cluster scenario). This is selected by a pattern-matching algorithm minimising algorithm minimising root mean square differences between the cluster’s “centre of gravity” “centre of gravity” of the cluster and each member. The most representative member is chosen to symbolize the cluster. But it should not be used as a deterministic forecast, nor should it be seen as a substitute for the cluster average (cluster averages are not currently available as web charts but are available for download from MARS).
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- If the ensemble members fall into a few, well separated groups of “similar forecasts” ““similar forecasts”” (a multi-modal distributiondistribution”) the most representative members (cluster scenarios) will represent the range of possible weather conditions (see Fig813.BFig8.1.3.1-2).
- If the spread of all the ensemble members is broad and represent a continuum which does not divide up logically into groups, then the cluster algorithm cannot partition the ensemble into significant different clusters and the ensemble median is presented as the most representative member (cluster scenario) of the single "cluster".
- If the spread of all the ensemble members is small, more than one cluster can still be created provided that a partition is possible.
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The 1000hPa clusters correspond to the clusters the 500hPa fields; clustering is not done on the 1000hPa fields. Each cluster has the same population of members and the same most representative member. Major differences might be due to the relation between the flow at 500hPa and that at 1000hPa – fairly similar 500hPa patterns might be linked to quite different 1000hPa patterns.
Fig813.BFig8.1.3.1-2: The most representative 500hPa members selected to describe the clustering of the forecast DT 00UTC 12 March 2017, T+120 to T+168 hours. Here there are 3 clusters (one per row). The most representative member or cluster scenario is the member of the cluster which has the minimum difference from the RMS of the cluster members. On 500hPa cluster scenario charts, shading denotes the 500mb height anomaly , that is difference between the instantaneous 500mb of the ensemble member height field for that member (i.e. as is contoured) and from the long term climatological average 500mb height field for that time of year.
In Fig813.B Fig8.1.3.1-2 there are three clusters:
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The web site includes cluster products equivalent to Fig813.B Fig8.1.3.1-2 for each of the four predefined lead-time windows. For additional information, the 1000hPa geopotential fields are also provided for each ensemble scenario to show the corresponding near-surface evolution. Users should note that for these the clustering has been made on the 500hPa fields, not the 1000hPa fields. Whilst the user should not treat the most representative members as deterministic solutions, it can nonetheless be helpful to examine the details of the evolution in such members, to see how a particular scenario can plausibly arise and evolve. One good way to do this is to use the cyclone database products presented by the extratropical extra-tropical cyclone diagrams, specifically the animations, at 12 hour intervals, of synoptic patterns for individual members.
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So, after clustering by weather scenario, each most representative member is then attributed to one of four large scale climatological weather regimes. These have been evaluated over an area covering Europe and the north Atlantic; an area considerably larger area than that used for the weather scenario clustering (see above and Fig813.AFig8.1.3.1-1).
Fig813.CFig8.1.3.1-3: The Euro-Atlantic area considered for the computation of four weather regimes (NAO+, NAO-, BL, AR) derived from reanalysis of 500hPa geopotential height.
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- Positive North Atlantic Oscillation (regime 1 or +NAO; conventional colour Blue).
- Euro-Atlantic blocking (regime 2 or BL; conventional colour Red).
- Negative North Atlantic Oscillation (regime 3 or -NAO; conventional colour Green).
- Pronounced ridge over the Atlantic (regime 4 or AR; conventional colour Violet).
Fig813.DFig8.1.3.1-4: The large scale climatological regimes for 500hPa heights computed for the cold season (October to April).
Fig813.EFig8.1.3.1-5: The large scale climatological regimes for 500hPa heights computed for the warm season (May to September).
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Each most representative member is attributed to one of four large scale climatological weather regimes by a pattern-matching algorithm which assigns it to the closest large scale climatological weather regime (by minimising root mean square differences). This attribution is indicated by the colour of the frame surrounding each cluster scenario (Blue:+NAO; Green:–NAO; Red:BL; Purple:AR). The climatological weather regime refers only to the displayed most representative member.
Fig813.F: As Fig813.B Fig8.1.3.1-6 As Fig8.1.3.1-2 but referring to the forecast DT 00UTC 05 March 2017, T+264 to T+360hr. The colour of the frame surrounding each most representative member indicates the large scale climatological weather regimes to which it has been attributed. On 500hPa plots such as these, shading denotes anomalies relative to climatology (as in Fig813.BFig8.1.3.1-2).
Flow dependent skill
It has been found:
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