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If time, 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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| Anchor | forclusternumber | forclusternumber | To change the number of clusters please see the Appendix.
Exercise 5: Assessment of forecast errors
In this exercise, the analyses covering the forecast period are now available to see how Nadine and the cut-off low actually behaved.
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For those interested: The code that computes the clusters can be found in the Python script: This uses the 'ward' cluster method from SciPy. Other cluster algorithms are available. See http://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html#scipy.cluster.hierarchy.linkage The python code can be changed to a different algorithm or the more adventurous can write their own cluster algorithm! |
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Exercise 5: Assessment of forecast errors
In this exercise, the analyses covering the forecast period are now available to see how Nadine and the cut-off low actually behaved.
Various methods for presenting the forecast error are used in the tasks below. The clusters created in the previous exercise can also be used.
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clusterNum=2 |
to
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clusterNum=3 |
Now if you run the eof.mv macro, it will generate a text file, such as ens_oper.eof.txt with 3 lines, one for each cluster. It will also show the 3 clusters as different colours.
You can use the 3 clusters in the cluster_to_ref.mv macro, for example:
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param="z500.s"
expId="ens_oper"
members_1=["cl.eof.1"]
members_2=["cl.eof.3"] |
would plot the mean of the members in the first and the third clusters (it's not possible to plot all three clusters together).
You can have as many clusters as you like but it does not make sense to go beyond 3 or 4 clusters.
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| title | Cluster method code |
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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 SciPy. Other cluster algorithms are available. See http://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html#scipy.cluster.hierarchy.linkage
=3 |
Now if you run the eof.mv macro, it will generate a text file, such as ens_oper.eof.txt with 3 lines, one for each cluster. It will also show the 3 clusters as different colours.
You can use the 3 clusters in the cluster_to_ref.mv macro, for example:
| Code Block |
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param="z500.s"
expId="ens_oper"
members_1=["cl.eof.1"]
members_2=["cl.eof.3"] |
would plot the mean of the members in the first and the third clusters (it's not possible to plot all three clusters together).
You can have as many clusters as you like but it does not make sense to go beyond 3 or 4 clusters.
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Further reading
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