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| title | Plot ensemble and cluster maps |
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As described in task 1, use the cluster definition file computed by eof.mv to the plot ensembles and maps with clusters enabled. 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 the two clusters created by the EOF/PCA analysis (equivalent to Figure 7 in Pantillon et al.) Edit cluster_to_an.mv and set: | Code Block |
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| #ENS members (use ["all"] or a list of members like [1,2,3]
members_1=["cl.eof.1"]
members_2=["cl.eof.2"] |
Run the macro to plot: z500, MSLP, tp over France and PV/320K. |
From Figure 7 in Pantillon et al. we see that cluster 1 corresponds to a cutoff low moving eastward over Europe and cluster 2 to a weak ridge over western Europe. Cluster 1 exhibits a weak interaction between Nadine and the cut-off low over Europe. In cluster 2, there is a strong interaction between the cutoff and Nadine in which Nadine makes landfall over the Iberian penisula.
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Q. How similar is the PCA computed clusters to your manual clustering?
Q. Change the date/time used to compute the clusters. How does the variance explained by the first two clusters change? Is geopotential the best parameter to use? |
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For those interested: The code that computes the clusters can be found in the Python script: aux/cluster.py. This uses the 'ward' cluster method from Sci |
Exercise 5. Exploring the role of uncertainty
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