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In 2012, at the time of this case study, ECMWF operational forecasts consisted of:
- HRES : spectral T1279 (18km 16km grid) highest resolution 10 day deterministic forecast.
- ENS : spectral T639 (36km 31km grid) resolution ensemble forecast (50 members) is run for days 1-10 of the forecast, T319 (70km) is run for days 11–15.
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The ECMWF operational deterministic forecast is called HRES. At the time of this case study, the model ran with a spectral resolution of T1279, equivalent to 18km 16km grid spacing.
Only a single forecast is run at this resolution as the computational resources required are demanding. The ensemble forecasts are run at a lower resolution.
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If your cluster definition file is called 'ens_oper_cluster.example.txt', then Edit cluster_to_anref.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"] |
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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. |
Use the clusters of ensemble members you have created in ens_oper_cluster.example.txt.
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Repeat using mapType=2 to see the smaller region over France.
These different regions will be used in the following exercises.
Animate the storm on this smaller geographical map.
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To change the number of clusters created by the EOF analysis, edit eof.mv. Change:
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clusterNum=2 |
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