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This exercise repeats some of the plots from the previous one but this time with clustering enabled.

Using clustering will highlight the clusters when plotting the ensemble members.

In this exercise you will:

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Panel
titleCreate your own clusters

Right-click 'ens_oper_cluster.example.txt' and select Edit (or make a duplicate)

The file contains two example lines:

Code Block
#1    2  3  4 9 22 33 40
#2    10 11 12 31 49

The first line defines the list of members for 'Cluster 1': in this example, members 2, 3, 4, 9, 22, 33, 40.

The second line defines the list of members for 'Cluster 2': in this example, members 10, 11, 12, 31, 49.

Change these two lines!.
Put your choice of ensemble member numbers for cluster 1 and 2 (lines 1 and 2 respectively).

You can create multiple cluster definitions by using the 'Duplicate' menu option to make copies of the file for use in the plotting macros..

The filename is important!
The first part of the name 'ens_oper' refers to the ensemble dataset and must match the name used in the plotting macro. 
The 'example' part of the filename can be changed to your choice and should match the 'clustersId'.
As an example a filename of: ens_both_cluster.fred.txt would require 'expId=ens_both', 'clustersId=fred' in the macro.

Use your own clusters:

RMSE curves

lot the:

RMSE curves

Stamp maps

with clusters enabled.

To enable clusters in the macros:

(TO BE DONE)

Use clusters_to_an.mv with user defined clusters.

Panel
titlePlot maps 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:

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.

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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. Are two clusters enough? Where do the extreme forecasts belong?

Task 2: Empirical orthogonal functions / Principal component analysis

This task provides a A quantitative way of clustering an ensemble is by computing empirical orthogonal functions from the differences between the ensemble members and the control forecast.

Although geopotential height at 500hPa at 00 24/9/2012 is used in the paper by Pantillon et al., the steps described below can be used for any parameter at any step.

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Panel
titlePlot cluster maps

 The cluster_to_an.mv macro will use the clustering information and

Set the parameter to that used in eof.mv

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Q1. What do the EOFs plotted by eof.mv show?
2 Q. Change the parameter used for the EOF (try the 'total precipitation' field). How does the cluster change?

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