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Further analysis using ensembles

If time:

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titleUse pf_to_cf_diff.mv to compare two ensemble members to the control forecast

This will show the forecasts from the ensemble members and also their difference with the ensemble control forecast.

To animate the difference in MSLP with ensemble members '30' and '50', set:

Code Block
param="mslp"
pf=[30,50]

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Q. Compare the CDF from the different forecast ensembles; what can you say about the spread?

Forecasting during HyMEX : Work in teams for group discussion

Ensemble forecasts can be used to help forecasting. This exercise discusses a real-world case of forecasting during HyMEX.

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titleForecast exercise
Please see separate handout for forecasting exercise.

 

Exercise 4: Cluster analysis

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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.

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titlePlot maps 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:

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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titlePlot maps with clusters

The macro cluster_to_an.mv can be used to plot maps of parameters 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"]
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Q. Experiment with the choice of members in each clusters and plot z500 at

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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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