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Available plot types

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this will plot (a) the mean of the ensemble forecast, (b) the ensemble spread, and (c) the HRES deterministic forecast.


this plots a 'spaghetti map' for a given parameter for the ensemble forecasts compared to the reference HRES forecast. Another way of visualizing ensemble spread. 



 this plots a vertical cross-section through the forecasts in the same way as the cross-section plots for the analyses.

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 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.

Additional plots for further analysis:

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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.

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this will plot the difference between the ensemble control, ensemble mean or an individual ensemble member and the HRES forecast for a given parameter.

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this comprehensive macro
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produces a single map for a given parameter. The map can be either: i/ the ensemble mean, ii/ the ensemble spread, iii/ the control forecast, iv/ a specific perturbed forecast, v/ map of the ensemble probability subject to a threshold, vi/ ensemble percentile map for a given percentile value. For example, it is possible to plot of a map showing the probability that MSLP would be below 995hPa.

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this macro can be used to plot the difference for two ensemble members against the HRES forecasts. As ensemble perturbations are applied in +/- pairs, using this macro it's possible to see the nonlinear development of the members and their difference to the HRES forecast.


Group working

If working in groups, each group could follow the tasks below with a different ensemble forecast. e.g. one group uses the 'ens_oper', another group uses 'ens_2016'.

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titleQuestions to consider

Q. How does the ensemble mean MSLP and Z500 fields compare to the HRES forecast?
Q. Examine the initial diversity in the ensemble and how the ensemble spread and error growth develops.  What do the extreme forecasts look like?
Q. Are there any members that consistently provide a better forecast?
Q. Comparing the two ensembles, ens_oper and ens_2016, which is the better ensemble for this case study?

Task 1: Ensemble spread

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Use the ens_to_anmaps.mv (rename as ens_mean_spread.mv)icon and plot the MSLP and z500. This will produce plots showing: the mean of all the ensemble forecasts, the spread of the ensemble forecasts and the operational HRES deterministic forecast.

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Code Block
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titleUse all ensemble members in this task:
#ENS members (use ["all"] or a list of members like [1,2,3]
members=["all"]        #[1,2,3,4,5] or ["all"] or ["cl.example.1"]


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Q. Does When does the ensemble spread capture grow the error in fastest during the forecast?Q. What other comments can you make about the ensemble spread?

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Spaghetti plots - another way to visualise spread

Warning

TODO: remove analysis from the spaghetti plots, plot HRES instead & rename ens_spag.mv

A "spaghetti" plot is where a single contour of a parameter is plotted for all ensemble members. It is another way of visualizing the differences between the ensemble members and focussing on features.

Use the ens_to_anref_runs_spag.mv (rename ens_spag.mv & remove AN)  icon. Plot and animate the MSLP and z500 fields using your suitable choice for the contour level. Find a value that highlights the low pressure centres. Note that not all members may reach the low pressure set by the contour.

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Experiment with changing the contour value and (if time) plotting other fields.

Task

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2: Visualise ensemble members

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.

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This can help in identifying individual ensemble members that produce a better forecast than the control or HRES forecast.

Icons ens_to_anref_diff.mv (rename ens_to_hres_diff.mv and replace 'an' with 'hres') and pf_to_cf_diff.mv can be used to compare ensemble members.

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TODO:

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titleUse ens_to_anref_diff to compare an ensemble member to the analysis

 To animate the difference in MSLP of an individual ensemble member 30 to the analysis, edit the lines:

Code Block
param="mslp"
ensType="pf30"

and visualise the plot.

To compare the control forecast, change:

Code Block
ensType="cf"


Further analysis using ensembles

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  • Select 'better' forecasts using the stamp plots and use ens_to_anref.mv to modify the list of ensemble plots.
  • Use pf_to_cf_diff.mv and ens_to_anref_diff.mv to take the difference between these perturbed ensemble member forecasts from the control and analyses to also look at this.

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