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Q. What do you think about the quality of this forecast? And why? |
Exercise 3 :
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Operational ensemble forecasts
Recap
- ECMWF operational ensemble forecasts treat uncertainty in both the initial data and the model.
- Initial analysis uncertainty: sampled by use of Singular Vectors (SV) and Ensemble Data Assimilation (EDA) methods. Singular Vectors are a way of representing the fastest growing modes in the initial state.
- Model uncertainty: sampled by use of stochastic parametrizations. In IFS this means the 'stochastically perturbed physical tendencies' (SPPT) and the 'spectral backscatter scheme' (SKEB)
- Ensemble mean : the average of all the ensemble members. Where the spread is high, small scale features can be smoothed out in the ensemble mean.
- Ensemble spread : the standard deviation of the ensemble members, represents how different the members are from the ensemble mean.
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Experiment with changing the contour value and (if time) plotting other fields.
Task 4: Visualise ensemble members
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Stamp maps are used to visualise all the ensemble members as normal maps. These are small, stamp sized contour maps plotted for each ensemble member using a small set of contours.There are two icons to use, stamp.mv and stamp_diff.mv.
Use stamp.mv to plot the MSLP and z500 fields in the ensemble.
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Note, stamp_diff.mv cannot be used for 'tp' as there is no precipitation data in the analyses.
Difference stamp maps
Use the stamp_diff.mv plot to look at the differences between the ensemble members and the analysis. It can be easier to understand the difference in the ensembles by using difference stamp maps.
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no precipitation data in the analyses
Compare ensemble members to analysis
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hres_rmse.mv : this plots the root-mean-square-error growth curves for the operational HRES forecast compared to the ECMWF analyses. hres_to_an_diff.mv : this plots a single parameter as a difference map between the operational HRES forecast and the ECMWF analysis. Use this to understand the forecast errors. |
Task 1: Forecast error
In this task, we'll look at the difference between the forecast and the analysis by using "root-mean-square error" (RMSE) curves as a way of summarising the performance of the forecast.
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Q. What do the RMSE curves show? |
Task 2: Compare forecast to analysis
Use the hres_to_an_diff.mv icon and plot the difference map between the HRES forecast and the analysis for z500 and mslp.
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If time: look at other fields to study the behaviour of the forecast.
Task 3: RMSE "plumes" for the ensemble
This is similar to task 1 in exercise 2, except the RMSE curves for all the ensemble members from a particular forecast will be plotted.
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- Explore the plumes from other variables.
- Do you see the same amount of spread in RMSE from other pressure levels in the atmosphere?
Task 4: Difference stamp maps
Use the stamp_diff.mv plot to look at the differences between the ensemble members and the analysis. It can be easier to understand the difference in the ensembles by using difference stamp maps.
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Q. Using the stamp and stamp difference maps, study the ensemble. Identify which ensembles produce "better" forecasts. |
Appendix
Further reading
For more information on the stochastic physics scheme in (Open)IFS, see the article:
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