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Q. How do the HRES, ensemble control forecast and ensemble mean compare?
Q. How do the ensemble members behave, do they give better or worse forecasts?
Q. Compare with ens_2016 ensemble, or with a different group. How does the performance of the HRES and control forecast change?

There might be some evidence of clustering in the ensemble plumes.

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Warning

Always first use the eof.mv first for a given parameter, step and ensemble forecast (e.g. ens_oper or ens_2016) to create the cluster file.

Otherwise cluster_to_an.mv and other plots with clustering enabled will fail or plot with the wrong clustering of ensemble members.

If you change step or ensemble, recompute the EOFS and cluster definitions using eof.mv. Note however, that once a cluster has been computed, it can be used for all steps with any parameter.

If you rerun the eof.mv macro, it will write to a new file called for example 'ens_oper.eof.txt.latest' if the original file still exists. Make sure you rename this file to 'ens_oper.eof.txt' otherwise the plotting macros will continue to use the original ens_oper.eof.txt.


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titleCompute EOFs and clusters

Edit 'eof.mv'

Set the parameter to use, choice of ensemble and forecast step required for the EOF computation:

Code Block
param="z500"
expId="ens_oper"
steps=[2012-09-24 00:00]

Run the macro.

The above example will compute the EOFs of geopotential height anomaly at 500hPa using the 2012 operational ensemble at forecast step 00Z on 24/09/2012.

A plot will appear showing the first two EOFs (similar to Figure 5 in Pantillon et al.)

The geographical area for the EOF computation is: 35-55N, 10W-20E (same as in Pantillon et al). If desired it can be changed in eof.mv.

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titleEOF cluster definition file

The eof.mv macro will create a text file with the cluster definitions, in the same format as described above in the previous task.

The filename will be different, it will have 'eof' in the filename to indicate it was created by using empirical orthogonal functions.

Code Block
languagebash
titleCluster filename created for ensemble 'ens_oper' using eof.mv
ens_oper_cluster.eof.txt

If a different ensemble forecast is used, for example ens_2016, the filename will be: ens_2016_cluster.eof.mv

This cluster definition file can then be used to plot any variable at all steps (as for task 1).

Info

If you rerun the eof.mv macro, it will write to a new file called for example 'ens_oper.eof.txt.latest' if the original file still exists. Make sure you rename this file to 'ens_oper.eof.txt' otherwise the plotting macros will continue to use the original ens_oper.eof.txt.


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Q. What do the EOFs plotted by eof.mv show?
Q. Change the parameter used for the EOF (try the ' total precipitation 'tp' field). How does the cluster change?

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Q. How similar is the PCA computed clusters to your manual clustering?
Q. Which cluster best represents the analysis?
Q. How useful is the cluster analysis as an aid to forecasting for HyMEX?
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?
Q. Compare ens_oper and ens_2016 (or with a different group). How do the clusters differ and why?


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titleChanging the number of clusters

To change the number of clusters created by the EOF analysis, find the file in the folder 'base' called base_eof.mv.

Edit this file and near the top, change:

Code Block
  clusterNum=2

to

Code Block
  clusterNum=3

then select 'File' and 'Save' to save the changes.

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_an.mv macro, for example:

Code Block
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).

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