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Panel
titlePlot ensembles with your cluster definitions

Use the clusters of ensemble members you have created in ens_oper_cluster.example.txt.

Set clustersId='example' in each of the ensemble plotting macros to enable cluster highlighting.

Replot ensembles:

RMSE: plot the RMSE curves using ens_rmse.mv. This will colour the curves differently according to which cluster they are in.

Stamp maps: the stamp maps will be reordered such at the ensemble members will be groups according to their cluster. Applies to stamp.mv and stamp_diff.mv. This will make it easier to see the forecast scenarios according to your clustering.

Spaghetti maps: with clusters enabled, two additional maps are produced which show the contour lines for each cluster.

Panel
titlePlot maps with of parameters as clusters

The macro cluster_to_an.mv can be used to plot maps of parameters as clusters and compared to the analysis and HRES forecasts.

Use cluster_to_an.mv to plot z500 maps of your two clusters (equivalent to Figure 7 in Pantillon et al.)

If your cluster definition file is called 'ens_oper_cluster.example.txt', then Edit cluster_to_an.mv and set:

Code Block
languagebash
#ENS members (use ["all"] or a list of members like [1,2,3]
members_1=["cl.example.1"]
members_2=["cl.example.2"]

If your cluster definition file is has another name, e.g. ens_oper_cluster.fred.txt, then members_1=["cl.fred.1"].

Plot other parameters:

Plot total precipitation for France (mapType=2).

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Q. Experiment with the choice of members in each clusters and plot z500 at t+96 (Figure 7 in Pantillon et al.). How similar are your cluster maps?
Q. What date/time does the impact of the different clusters become apparent?
Q. Are two clusters enough? Where do the extreme forecasts belong?

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