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Using the probability map, click the cursor data icon 
and move the pointer over the map for +96h and select choose a location in the region of highest rainfall. Do this for both the 2012 or and 2016 ensemble map.
Make a note of the latitude and longitude coordinates. The highest rainfall area was approximately over the Cévennes mountains , approximately ( 44°25′34″N 03°44′21″E ).
Edit prob_tp_compare.mv and set the location:
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and replot the map. A small purple dot will appear at the location specified. If the dot is not in the right location, change it and replot.
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You should now have the probability values that total precipitation will exceed 10mm, 20mm and 30mm, for both the 2012 and 2016 ensembles, for forecast time +96 hours for your chosen location.
Task 2: Plot the CDF
This exercise uses the cdf.mv icon.
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Set the percentile for the total precipitation to 70% and specify the location as in Task Tasks 1 & 2:
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#The percentile of ENS precipitation forecast perc=70 location=[44.0,4.1] # [ lat, lon ] -- use your own values! |
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Q. From the CDF and probabilities maps, which ensemble forecast shows increased probability of precipitation higher than 10mm? |
Exercise 5: Cluster analysis
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- Construct your own qualitative clusters by choosing members for two clusters.
- Generate clusters using principal component analysis.
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You can choose any parameter to construct the clusters from, if you think another parameter shows a clear clustering behaviour.
How to create your own cluster
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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.
Plot
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maps of parameters as clusters
The macro cluster_to_ref.mv can be used to plot maps of parameters as clusters and compared to the ensemble control forecast and the HRES forecast.
Use cluster_to_ref.mv to plot z500 maps of your two clusters.
If your cluster definition file is called 'ens_oper_cluster.example.txt', then Edit cluster_to_an.mv and set:
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#ENS members (use ["all"] or a list of members like [1,2,3]
members_1=["cl.example.1"]
members_2=["cl.example.2"] |
If your cluster definition file is has another name, e.g. ens_oper_cluster.fred.txt, then members_1=["cl.fred.1"].
Plot ensembles with clusters
In this part of the task, redo the plots from the previous exercise which looked at ways of plotting ensemble data, but this time with clustering enabled.
Stamp maps: the stamp maps will be reordered such at the ensemble members will be grouped according to their cluster. 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.
Use the clusters of ensemble members you have created in ens_oper_cluster.example.txt.
Set clustersId='example' in each of the stamp.mv and ens_to_ref_spag.mv to enable cluster highlighting.
If time, also try the ens_part_to_all.mv icon. This compares the spread and mean of part of the ensemble to the full ensemble.
Plot other parameters
Use the stamp.mv icon and change it to plot the total precipitation over France with clusters enabled.e.g.
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param="tp"
expId="ens_oper"
mapType=2
clustersId="example" |
If you choice of clustering is accurate, you should see a clear separation of precipitation over France between the two clusters
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 ensembles:
Stamp maps: the stamp maps will be reordered such at the ensemble members will be groups according to their cluster. Applies to stamp.mv icon. 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.
Plot maps of parameters as clusters
| Warning |
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TODO: add image of cluster_hres.mv (renamed from cluster_to_an.mv) here. |
The macro cluster_to_an.mv can be used to plot maps of parameters as clusters and compared to the HRES forecasts. TODO: rename cluster_to_an.mv to cluster_hres.mv and comment out plot of AN.
Use cluster_to_an.mv to plot z500 maps of your two clusters.
If your cluster definition file is called 'ens_oper_cluster.example.txt', then Edit cluster_to_an.mv and set:
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#ENS members (use ["all"] or a list of members like [1,2,3]
members_1=["cl.example.1"]
members_2=["cl.example.2"] |
If your cluster definition file is has another name, e.g. ens_oper_cluster.fred.txt, then members_1=["cl.fred.1"].
Plot other parameters:
Plot total precipitation for France (mapType=2). Compare with Figure 8. in Pantillon et al.
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Q. What date/time does the impact of the different clusters become apparent? |
Task 2: Empirical orthogonal functions / Principal component analysis
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stamp.mv : this plots all of the ensemble forecasts for a particular field and lead time. Each forecast is shown in a stamp sized map. Very useful for a quick visual inspection of each ensemble forecast.'ens_oper_cluster.example.txt',