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After a few seconds, this will generate a map showing two parameters: mean-sea-level pressure (MSLP) and 3hrly max wind-gust at 10m (wgust10).
Note that as only 6hrly wind gust data is available from the operational forecasts, we have supplemented the 3hrly fields using forecast data
Use the play button 
to animate the map and follow the development and track of the storm.
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Exercise 3 : The operational ensemble forecasts
Recap
- Sources of forecast uncertainty: initial analysis and model errorECMWF 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.
- Model uncertainty: sampled by use of stochastic processes. parametrizations In IFS this means Stochastically Perturbed Physical Tendencies (SPPT) and the spectral backscatter scheme (SKEB)
- Singular Vectors: a way of representing the fastest growing modes in the initial state.
- 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 and represents how different the members are from the ensemble mean.
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THIS NEEDS IMPROVING
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For these exercises please use the Metview icons in the row labelled 'ENS'. ens_rmse.mv : this is similar to the oper_rmse.mv in the previous exercise. It will plot the root-mean-square-error growth for the ensemble forecasts. ens_to_an.mv : this will plot (a) the mean of the ensemble forecast, (b) the ensemble spread, (c) the HRES deterministic forecast and (d) the analysis for the same date. stamp.mv : this plots all of the ensemble forecasts for a particular field and lead time. Each forecast is shown in a stamp sized map. Very useful for a quick visual inspection of each ensemble forecast. stamp_diff.mv : similar to stamp.mv except that for each forecast it plots a difference map from the analysis. Very useful for quick visual inspection of the forecast differences of each ensemble forecast. ens_to_an_runs_spag.mv : this plots a 'spaghetti map' for a given parameter for the ensemble forecasts compared to the analysis. Another way of visualizing ensemble spread.
Additional plots for further analysis:
ens_1x1.mv : this plots a single map of a single ensemble member, the mean or the spread. pf_to_cf_diff.mv : this useful macro allows two individual ensemble forecasts to be compared to the control forecast. As well as plotting the forecasts from the members, it also shows a difference map for each. |
Getting started
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Task 1: RMSE "plumes"
This is similar to task 1 in exercise 2, except now the RMSE curves for all the ensemble members from a particular forecast will be plotted. All 4 forecast dates are shown.
Using the ens_rmse.mv icon, right-click, select 'Edit' and plot the curves for 'mslp' and 'wgust10'. Note this is only for the European region. The option to plot over the larger geographical region is not available.
Q. What features can be noted from these plumes?
Q. How do these change with different forecast lead times?
Note there appear to be some forecasts that give a much lower RMS error. We will come back to this in later tasks.
If time, explore the plumes from other variables. Do you see the same amount of spread in RMSE from other pressure levels higher in the atmosphere?
Task 2: Ensemble spread
In the previous task, we have seen that introducing some uncertainty into the forecast
Suggested plots:
- 4 per frame: analysis, ensemble mean, spread and 1 other.
- spaghetti plots (multiple plots per frame)
- stamp plots.
- Observations of wind gust + analysis (faked): 1 day only.
Note that as only 6hrly wind gust data is available from the operational forecasts, we have supplemented the 3hrly fields using forecast data
Task ??. CDF/RMSE at different locations <<< WHERE DOES THIS FIT???
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