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The aim of this exercise is to use the same visualisation and investigation to understand the impact the different types of uncertainty make on the forecast.
Follow the same tasks as above.
Task 1.
Objective: Understand the impact of changing the ensemble uncertainty
Look at ensemble mean and spread for all 3 cases.
- How does it vary?
- Which gives the better spread?
- How does the forecast change with reducing lead time?
Task 2.
Look at different ensemble sizes?
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For this exercise, we suggest either each team focus on one of the above experiments and compare it with the operational ensemble. Or, each team member focus on one of the experiments and the team discuss and compare the experiments. |
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The different macros available for this exercise are very similar to those in previous exercises.
For this exercise, use the icons in the row labelled 'Experiments'. These work in a similar way to the previous exercises. ens_exps_rmse.mv : this will produce RMSE plumes for all the above experiments and the operational ensemble. ens_exps_to_an_spag.mv : this will produce spaghetti maps for a particular parameter contour value compared to the analysis. ens_exps_to_an.mv : this produces 4 plots showing the ensemble spread from the OpenIFS experiments compared to the analysis. ens_part_to_all.mv : this allows the spread & mean of a subset of the ensemble members to be compared to the whole ensemble. |
Task 1. RMSE plumes
Use the ens_exps_rmse.mv icon and plot the RMSE curves.
Q. Compare the spread from the different experiments. Is it what you would expect?
Q. The OpenIFS experiments were at a lower horizontal resolution. How does the RMSE spread compare between the 'ens_oper' and 'ens_both' experiments?
If time: change the 'run=' line to select a different forecast lead-time (run).
Task 2. Ensemble spread
Use the ens_exps_to_an.mv icon and plot the ensemble spread for the different experiments.
Q. How does change the representation of uncertainty affect the spread?
Q. Which experiment gives the better spread?
Q. Is it possible to determine whether initial uncertainty or model uncertainty is more or less important in the forecast error?
If time:
- change the 'run=' line to select a different forecast lead-time (run).
- use the ens_part_to_all.mv icon to compare a subset of the ensemble members to that of the whole ensemble for an experiment.
(Task 3. THIS STILL NEEDS WORK)
- Find an ensemble member that gives the best 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. How does this differ between the 3 OpenIFS runs? (with model uncertainty only, each initial state is identical so differences will develop from
(Task 4. THIS STILL NEEDS WORK)
Ensemble perturbations are applied in positive and negative pairs. For each perturbation computed, the initial fields are CNTL +/- PERT. (need a diagram here)
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- Plot PV at 330K. What are the differences between the forecast? Upper tropospheric differences played a role in the development of this shallow fast moving cyclone.
extra notes
Plots:
- as above
- 4 frame: fc-an, fc-fc, pert.fc, ctl-an? *(compare the fc-fc maps with fc-an maps - can we see the uncertainty in the difference?)PV maps
Further reading
For more information on the stochastic physics scheme in (Open)IFS, see the article:
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