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IntroductionThe ECMWF operational ensemble forecasts for the western Mediterranean region exhibited high uncertainty while Hurricane Nadine was slowly moving over the eastern North Atlantic in September 2012. Interaction with an Atlantic cut-off low produced a bifurcation in the ensemble and significant spread, influencing both the track of Hurricane Nadine and the synoptic conditions downstream. The HyMEX (Hydrological cycle in Mediterranean eXperiment) field campaign was also underway and forecast uncertainty was a major issue for planning observations during the first special observations period of the campaign. This interesting case study examines the forecasts in the context of the interaction between Nadine and the Atlantic cut-off low in the context of ensemble forecasting. It will explore the scientific rationale for using ensemble forecasts, why they are necessary and how they can be interpreted, particularly in a "real world" situation of forecasting for an observational field campaign.
In this case studyIn the exercises for this interesting case study we will:
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- Control forecast (unperturbed)
- Perturbed ensemble members. Each member will use slightly different initial data conditions and include model uncertainty pertubations.
2012 Operational ensemble
ens_oper: This dataset is the operational ensemble from 2012 and was used in the Pantillon et al. publication. A key feature of this ensemble is use of a climatological SST field (seen in the earlier tasks).
2016 Operational ensemble
ens_2016: This dataset is a reforecast of the 2012 event using the ECMWF operational ensemble of March 2016.
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The analysis was not rerun for 20-Sept-2012. This means the 2016 reforecast using the 2016 ensemble will be using the original 2012 analyses. Also only 10 ensemble data assimilation (EDA) members were used in 2012, whereas 25 are in use for 2016 operational ensembles, so each EDA member will be used multiple times for this reforecast. This will impact on the spread and clustering seen in the tasks in this exercise.
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This can help in identifying individual ensemble members that produce a different forecast than the control or HRES forecast.
Use the ens_to_ref_diff.mv icon to compare an ensemble member to the HRES forecast. Use pf_to_cf_diff.mv to compare ensemble members to the control forecast.
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To animate the difference in MSLP of an individual ensemble member 30 to the HRES forecast, edit the lines:
and visualise the plot. To compare the control forecast, change:
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Compare the SST parameter used for the ens_oper and ens_2016 ensemble forecasts. The 2016 reforecast of this case study used a coupled ocean model unlike the 2012 ensemble and HRES forecast that used climatology for the first 5 days.
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It is usual to create clusters from z500 as it represents the large-scale flow and is not a noisy field. However, for this particular case study, the stamp map of 'tp' (total precipitation) over France is also very indicative of the distinct forecast scenarios. To change the map geographical area see the Appendix.
You can choose any parameter to construct the clusters from, if you think another parameter shows a clear clustering behaviour.
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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).
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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 of a filename of: ens_both_cluster.fred.txt would require 'expId=ens_both', 'clustersId=fred' in the macro
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Set clustersId='example' in the stamp.mv to enable cluster highlighting.
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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 your choice of clustering is accurate, you should see a clear separation of precipitation over France between the two clusters.
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As a smooth dynamical field, geopotential height at 500hPa at 00Z 24/9/2012 is recommend recommended (it is used in the paper by Pantillon et al.), but the steps described below can be used for any parameter at any step.
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Always use the If you change step or ensemble, recompute the EOFS EOFs and cluster definitions using Note that the EOF analyses analysis is run over the smaller domain over France. This may produce a different clustering to your manual cluster if you used a larger domain. |
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Plot ensemble and cluster maps
Use the cluster definition file computed by eof.mv to the plot ensembles and maps with clusters enabled (as above, but this time with the 'eof' cluster file).
The macro cluster_to_ref.mv can be used to plot maps of parameters as clusters and compared to the HRES forecast.
Use cluster_to_ref.mv to plot z500 and MSLP maps of the two clusters created by the EOF analysis.
Edit cluster_to_ref.mv and set:
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#ENS members (use ["all"] or a list of members like [1,2,3] members_1=["cl.eof.1"] members_2=["cl.eof.2"] |
Run the macro.
If time also look at other parameters such as PV/320KPV320K.
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Q. What are the two scenarios proposed by the two clusters? |
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