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The paper by Pantillon et al, describes the use of clustering to identify the main scenarios among the ensemble members.
This exercise repeats some of the plots from the previous one but this time with clustering enabled.
In this exercise you will:
- Construct your own qualitative clusters by choosing members for two clusters
- Generate clusters using principal component analysis (similar to Pantillon et al).
Task 1: Create own clusters
Clusters can be created by hand manually from lists of the ensemble members.
Refer back to the plots from the previous exercise to choose members for two clusters. The stamp maps are useful for this task.
From the stamp.map of z500 at 24/9/2012 (t+96), identify ensemble members that represent the two most likely forecast scenarios. 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.
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Right-click 'ens_oper_cluster.example.txt' and select Duplicate. Change the 'example' |
Create two clusters by:
(TO BE DONE)
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As part of this exercise you may have run OpenIFS yourself in the class to generate another ensemble; one participant per ensemble member.
Recap
- EDA is the Ensemble Data Assimilation.
- SV is the use of Singular Vectors to perturb the initial conditions.
- SPPT is the stochastic physics parametrisation scheme.
- SKEB is the stochastic backscatter scheme applied to the model dynamics.
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Using the macros provided:
- find Find an ensemble member(s) that gave a consistently improved forecast and take the difference from the control.
- Step back to the beginning of the forecast and look to see where the difference originates from.
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So, for each computed perturbation, two perturbed initial fields are created e.g. ensemble members 1 & 2 are a pair, where number 1 is a positive difference compared to the control and 2 is a negative difference.
- Choose an odd & even ensemble pair (use the stamp plots). Use the appropriate icon to compute the difference of the members from the ensemble control forecast.
- Study the development of these differences using the MSLP and wind fields. If the error growth is linear the differences will be the same but of opposite sign. Non-linearity will result in different patterns in the difference maps.
- Repeat looking at one of the other forecasts. How does it vary between the different forecasts?
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Appendix
Datasets available
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Further reading
For more information on the stochastic physics scheme in (Open)IFS, see the article:
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