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If time, change the date/time used to compute the clusters. How does the variance explained by the first two clusters change? Is geopotential the best parameter to use?

...

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Exercise 5: Assessment of forecast errors

In this exercise, the analyses covering the forecast period are now available to see how Nadine and the cut-off low actually behaved.

Various methods for presenting the forecast error are used in the tasks below.  The clusters created in the previous exercise can also be used.

Enter the 'Forecast errors' folder in the openifs_2018 folder to start work on this exercise.

Image Added

Task 1: Analyses from 20th September

Now look at the analyses from 20th September to observe what actually happened.

Right-click an_1x1.mv, Edit and set the plot to show MSLP and geopotential at 500hPa:

plot1=["z500.s","mslp"]

Click the play button and animate the plot to watch how Nadine and the cut-off low behave.

Drop the mv_track.mv icon to overlay the track of Nadine onto the map.

If time, use the other icons such as an_2x2.mv and an_xs.mv to look at the cross-section through the analyses and compare to the forecast cross-sections from the previous exercises.

Task 2: RMSE "plumes" for the ensemble

This is similar to the previous exercise, except the RMSE curves for all the ensemble members from a particular forecast will be plotted.

Right-click the ens_rmse.mv icon, select 'Edit' and plot the curves for 'mslp' and 'z500'.

Change 'expID' for your choice of ensemble (either ens_oper or ens_2016).

Clusters

First plot the plumes with clustering off:

clustersId="off"

There might be some evidence of clustering in the ensemble plumes.

There might be some individual forecasts that give a lower RMS error than the control forecast.

Next, use the cluster files created from the earlier exercise. You can use either your own created cluster file as before, or use the EOF generated file.

For example:

clustersId="eof"

would use the cluster definitions in the file: ens_oper_cluster.eof.txt (for the 2012 operational ensemble).

The cluster files are 'linked' from the Cluster folder, but if they do not work, just copy the cluster file (e.g. ens_oper_cluster.eof.txt) to the Forecast_errors folder.

Task 3: 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.

Note, stamp_diff.mv cannot be used for 'tp' as there is no precipitation data in the analyses.

Clustering can also be enabled for this task

Change:

  clusterNum=2

to

  clusterNum=3

Now if you run the eof.mv macro, it will generate a text file, such as ens_oper.eof.txt with 3 lines, one for each cluster. It will also show the 3 clusters as different colours.

You can use the 3 clusters in the cluster_to_ref.mv macro, for example:

param="z500.s"
expId="ens_oper"
members_1=["cl.eof.1"]
members_2=["cl.eof.3"]

would plot the mean of the members in the first and the third clusters (it's not possible to plot all three clusters together).

You can have as many clusters as you like but it does not make sense to go beyond 3 or 4 clusters.

Exercise 5: Assessment of forecast errors

In this exercise, the analyses covering the forecast period are now available to see how Nadine and the cut-off low actually behaved.

Various methods for presenting the forecast error are used in the tasks below.  The clusters created in the previous exercise can also be used.

Enter the 'Forecast errors' folder in the openifs_2018 folder to start work on this exercise.

Image Removed

Task 1: Analyses from 20th September

Now look at the analyses from 20th September to observe what actually happened.

Right-click an_1x1.mv, Edit and set the plot to show MSLP and geopotential at 500hPa:

plot1=["z500.s","mslp"]

Click the play button and animate the plot to watch how Nadine and the cut-off low behave.

Drop the mv_track.mv icon to overlay the track of Nadine onto the map.

If time, use the other icons such as an_2x2.mv and an_xs.mv to look at the cross-section through the analyses and compare to the forecast cross-sections from the previous exercises.

Task 2: RMSE "plumes" for the ensemble

This is similar to the previous exercise, except the RMSE curves for all the ensemble members from a particular forecast will be plotted.

Right-click the ens_rmse.mv icon, select 'Edit' and plot the curves for 'mslp' and 'z500'.

Change 'expID' for your choice of ensemble (either ens_oper or ens_2016).

Clusters

First plot the plumes with clustering off:

clustersId="off"

There might be some evidence of clustering in the ensemble plumes.

There might be some individual forecasts that give a lower RMS error than the control forecast.

Next, use the cluster files created from the earlier exercise. You can use either your own created cluster file as before, or use the EOF generated file.

For example:

clustersId="eof"

would use the cluster definitions in the file: ens_oper_cluster.eof.txt (for the 2012 operational ensemble).

The cluster files are 'linked' from the Cluster folder, but if they do not work, just copy the cluster file (e.g. ens_oper_cluster.eof.txt) to the Forecast_errors folder.

Task 3: 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.

Note, stamp_diff.mv cannot be used for 'tp' as there is no precipitation data in the analyses.

Clustering can also be enabled for this task.

Appendix

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To save any other images during these exercises for discussion later, you can either use:

"Export" button in Metview's display window under the 'File' menu to save to PNG image format. This will also allow animations to be saved into postscript.

or use the ksnapshot command to take a 'snapshot' of the screen and save it to a file.

If you want to create animations from other images, save the figures as postscript and then use the convert command:

convert -delay 75 -rotate "90<" in.ps out.gif

Back to the tutorial

Additional tasks of exercise 1

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Appendix

Anchor
saveimage saveimage

Saving images and animations

To save any other images during these exercises for discussion later, you can either use:

"Export" button in Metview's display window under the 'File' menu to save to PNG image format. This will also allow animations to be saved into postscript.

or use the ksnapshot command to take a 'snapshot' of the screen and save it to a file.

If you want to create animations from other images, save the figures as postscript and then use the convert command:

convert -delay 75 -rotate "90<" in.ps out.gif

Back to the tutorial

Additional tasks of exercise 1

Anchor
changemap changemap

Changing the map geographical area

Right-click on an_1x1.mv icon and select 'Edit'.

In the edit window that appears you can see the map types available covering a different area:

#Map type: 0=Atl-an, 1: Atl-fc, 2: France 
mapType=0

With mapType=0 , the map covers a large area centred on the Atlantic suitable for plotting the analyses and track of the storm (this area is only available for the analyses).

With mapType=1 , the map also covers the Atlantic

Right-click on an_1x1.mv icon and select 'Edit'.

In the edit window that appears you can see the map types available covering a different area:

#Map type: 0=Atl-an, 1: Atl-fc, 2: France 
mapType=0

With mapType=0 , the map covers a large area centred on the Atlantic suitable for plotting the analyses and track of the storm (this area is only available for the analyses).

With mapType=1 , the map also covers the Atlantic but a smaller area than for the analyses. This is because the forecast data in the following exercises does not cover as large a geographical area as the analyses.

...

Back to the tutorial

Additional tasks of exercise 4

To change the number of clusters created by the EOF analysis, edit eof.mv.

Change:

  clusterNum=2

to

  clusterNum=3

Now if you run the eof.mv macro, it will generate a text file, such as ens_oper.eof.txt with 3 lines, one for each cluster. It will also show the 3 clusters as different colours.

You can use the 3 clusters in the cluster_to_ref.mv macro, for example:

param="z500.s"
expId="ens_oper"
members_1=["cl.eof.1"]
members_2=["cl.eof.3"]

would plot the mean of the members in the first and the third clusters (it's not possible to plot all three clusters together).

You can have as many clusters as you like but it does not make sense to go beyond 3 or 4 clusters.

Back to the tutorial

Further reading

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We gratefully acknowledge the following for their contributions in preparing these exercises. From ECMWF: Glenn Carver, Gabriella Szepszo, Sandor Kertesz, Linus Magnusson, Iain Russell, Simon Lang, Filip Vana. From ENM/Meteo-France: Frédéric Ferry, Etienne Chabot, David Pollack and Thierry Barthet for IT support at ENM. 

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