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Key parameters: MSLP and z500.  We suggest concentrating on viewing these fields. If time, visualize other parameters (e.g. PV320K).

Available plot types

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'.

Choose your ensemble dataset by setting the value of 'expId', either 'ens_oper' or 'ens_2016' for this exercise.

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'.

Choose your ensemble dataset by setting the value of 'expId', either 'ens_oper' or 'ens_2016' for this exercise.

#The experiment. Possible values are:
# ens_oper = operational ENS
# ens_2016 = 2016 operational ENS

expId="ens_oper"

Ensemble forecast uncertainty

In these tasks, the performance of the ensemble forecast is studied.

Task 1: Ensemble spread

Use the ens_maps.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.

Change 'expId' if required to select either the 2012 ensemble expId="ens_oper" or the reforecast ensemble expId="ens_2016".

Animate this plot to see how the spread grows.

This macro can also be used to look at clusters of ensemble members. It will be used later in the clustering tasks. For this task, make sure all the members of the ensemble are used.

#The experiment. Possible values are:
# ens_oper = operational ENS
# ens_2016 = 2016 operational ENS

expId="ens_oper"

Ensemble forecast uncertainty

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#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"]

Task 1: Ensemble spread

Use the ens_maps.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.

Change 'expId' if required to select either the 2012 ensemble expId="ens_oper" or the reforecast ensemble expId="ens_2016".

Animate this plot to see how the spread grows.

Task 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.

Use stamp.mv to plot the MSLP and z500 fields in the ensemble.

The stamp map is slow to plot as it reads a lot of data. Rather than animate each forecast step, a particular date can be set by changing the 'steps' variableThis macro can also be used to look at clusters of ensemble members. It will be used later in the clustering tasks. For this task, make sure all the members of the ensemble are used.

#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"]

Task 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.

Use stamp.mv to plot the MSLP and z500 fields in the ensemble.

The stamp map is slow to plot as it reads a lot of data. Rather than animate each forecast step, a particular date can be set by changing the 'steps' variable.

#Define forecast steps
steps=[2012-09-24 00:00,"to",2012-09-24 00:00,"by",6]

Make sure clustersId="off" for this task. Clustering will be used later.

Compare ensemble members to the deterministic and control forecast

After visualizing the stamp maps, it can be useful to animate a comparison of individual ensemble members to the HRES and ensemble control deterministic forecasts.

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

Use the ens_to_ref_diff.mv icon to compare an ensemble member to the HRES forecast. Use pf_to_cf_diff.mv to compare ensemble members to the control forecast.

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

param="mslp"
ensType="pf30"

and visualise the plot.

To compare the control forecast, change:

ensType="cf"

#Define forecast steps
steps=[2012-09-24 00:00,"to",2012-09-24 00:00,"by",6]

Make sure clustersId="off" for this task. Clustering will be used later.

Compare ensemble members to the deterministic and control forecast

After visualizing the stamp maps, it can be useful to animate a comparison of individual ensemble members to the HRES and ensemble control deterministic forecasts.

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

Use the ens_to_ref_diff.mv icon to compare an ensemble member to the HRES forecast. Use pf_to_cf_diff.mv to compare ensemble members to the control forecast.

param="mslp"
ensType="pf30"

ensType="cf"

param="mslp"
pf=[30,50]

Sea-surface temperature

Compare the SST parameter used for the ens_oper and ens_2016 ensemble forecasts. The 2016 reforecast of this case study used a coupled ocean model unlike the 2012 ensemble and HRES forecast that used climatology for the first 5 days.

Exercise 4: Cluster analysis

The paper by Pantillon et al, describes the use of clustering to identify the main scenarios among the ensemble members.

Using clustering will highlight the ensemble members in each cluster in the plots.

In this exercise you will:

• Construct your own qualitative clusters by choosing members for two clusters.
• Generate clusters using principal component analysis.

Image Added

Enter the folder 'Clusters' in the openifs_2018 folder to begin working.

Task 1: Create your own clusters

Clusters can be created manually from lists of the ensemble members.

Choose members for two clusters. The stamp maps are useful for this task.

From the stamp map of z500 at 24/9/2012 (t+96), identify ensemble members that represent the two most likely forecast scenarios.

It is usual to create clusters from z500 as it represents the large-scale flow and is not a noisy field. However, for this particular case study, the stamp map of 'tp' (total precipitation) over France is also very indicative of the distinct forecast scenarios. 

You can choose any parameter to construct the clusters from, if you think another parameter shows a clear clustering behaviour.

How to create your own cluster

Image Added

Right-click 'ens_oper_cluster.example.txt' and select Edit (or make a duplicate)

The file contains two example

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This will show the forecasts from the ensemble members and also their difference with the ensemble control forecast.

To animate the difference in MSLP with ensemble members '30' and '50', set:

param="mslp"
pf=[30,50]

Sea-surface temperature

Compare the SST parameter used for the ens_oper and ens_2016 ensemble forecasts. The 2016 reforecast of this case study used a coupled ocean model unlike the 2012 ensemble and HRES forecast that used climatology for the first 5 days.

Exercise 4: Cluster analysis

The paper by Pantillon et al, describes the use of clustering to identify the main scenarios among the ensemble members.

Using clustering will highlight the ensemble members in each cluster in the plots.

In this exercise you will:

• Construct your own qualitative clusters by choosing members for two clusters.
• Generate clusters using principal component analysis.

Image Removed

Enter the folder 'Clusters' in the openifs_2018 folder to begin working.

Task 1: Create your own clusters

Clusters can be created manually from lists of the ensemble members.

Choose members for two clusters. The stamp maps are useful for this task.

From the stamp map of z500 at 24/9/2012 (t+96), identify ensemble members that represent the two most likely forecast scenarios.

It is usual to create clusters from z500 as it represents the large-scale flow and is not a noisy field. However, for this particular case study, the stamp map of 'tp' (total precipitation) over France is also very indicative of the distinct forecast scenarios. 

You can choose any parameter to construct the clusters from, if you think another parameter shows a clear clustering behaviour.

How to create your own cluster

Image Removed

Right-click 'ens_oper_cluster.example.txt' and select Edit (or make a duplicate)

The file contains two example lines:

1#   2  3  4  9  22 33 40
2#   10 11 12 31 49

...

Back to the tutorial

Additional tasks of exercise 2

Anchor
precipitation precipitation

Precipitation over France

Choose a hres macro (hres_1x1 or hres_2x2) and plot the total precipitation (parameter: tp), near surface wind field (parameter: wind10), relative humidity (parameter: r).

Change the area to France by setting 'maptype=2' in the macro script.

Back to the tutorial

Additional tasks of exercise 2

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Choose a hres macro (hres_1x1 or hres_2x2) and plot the total precipitation (parameter: tp), near surface wind field (parameter: wind10), relative humidity (parameter: r).

Change the area to France by setting 'maptype=2' in the macro script.

Back to the tutorial

...

Anchor
vertical vertical

Vertical structure and forecast evolution

This task focuses on the fate of Nadine and examines vertical PV cross-sections of Nadine and the cut-off low at different forecast times.

Right-click on the icon 'hres_xs.mv' icon, select 'Edit' and push the play Image Addedbutton.

The plot shows the cross-section for the 22nd September, (day 2 of the forecast), for potential vorticity (PV), wind vectors projected onto the plane of the cross-section and potential temperature drawn approximately through the centre of the Hurricane and the cut-off low. The red line on the map of MSLP shows the location of the cross-section.

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Changing forecast time

Cross-section data is only available every 24hrs until the 30th Sept 00Z (step 240).

This means the 'steps' value in the macros is only valid for the times:  [2012-09-20 00:00], [2012-09-21 00:00], ....  and so on to [2012-09-30 00:00].

Change the forecast time to day+6 (26th September). Nadine has now intensified as it approaches the coast.

steps=[2012-09-26 00:00]

Changing cross-section location

#Cross section line [ South, West, North, East ]
line = [30,-29,45,-15]

The cross-section location (red line) can be changed by editing the end points of the line as shown above.

If the forecast time is changed, the storm centres will move and the cross-section line will need to be repositioned to follow specific features. This is not computed automatically, but must be changed by altering the coordinates above. Use the cursor data icon Image Added to find the new position of the line.

Change the forecast time again to day+8 (28th September), or a different date if you are interested, relocate and plot the cross-section of Nadine and the low pressure system. Use the hres_1x1.mv icon from task 1 if you need to follow location of Nadine.

If time, try some of the other vertical cross-sections below.

Suggestions for other vertical cross-sections

A reduced number of fields is available for cross-sections compared to the isobaric maps: temperature (t), potential temperature (pt), relative humidity (r), potential vorticity (pv), vertical velocity (w), wind-speed (speed; sqrt(u*u+v*v)) and wind vectors (wind3).

Choose from the following (note the cross-section macro hres_xs.mv uses slightly different names for the parameters).

Back to the tutorial

Anchor
furtherplots furtherplots

Additional tasks of Exercise 3

Additional plots for further study

Right-click on the icon 'hres_xs.mv' icon, select 'Edit' and push the play Image Removedbutton.

The plot shows the cross-section for the 22nd September, (day 2 of the forecast), for potential vorticity (PV), wind vectors projected onto the plane of the cross-section and potential temperature drawn approximately through the centre of the Hurricane and the cut-off low. The red line on the map of MSLP shows the location of the cross-section.

Changing forecast time

Cross-section data is only available every 24hrs until the 30th Sept 00Z (step 240).

This means the 'steps' value in the macros is only valid for the times:  [2012-09-20 00:00], [2012-09-21 00:00], ....  and so on to [2012-09-30 00:00].

Change the forecast time to day+6 (26th September). Nadine has now intensified as it approaches the coast.

steps=[2012-09-26 00:00]

Changing cross-section location

#Cross section line [ South, West, North, East ]
line = [30,-29,45,-15]

The cross-section location (red line) can be changed by editing the end points of the line as shown above.

If the forecast time is changed, the storm centres will move and the cross-section line will need to be repositioned to follow specific features. This is not computed automatically, but must be changed by altering the coordinates above. Use the cursor data icon Image Removed to find the new position of the line.

Change the forecast time again to day+8 (28th September), or a different date if you are interested, relocate and plot the cross-section of Nadine and the low pressure system. Use the hres_1x1.mv icon from task 1 if you need to follow location of Nadine.

If time, try some of the other vertical cross-sections below.

Suggestions for other vertical cross-sections

A reduced number of fields is available for cross-sections compared to the isobaric maps: temperature (t), potential temperature (pt), relative humidity (r), potential vorticity (pv), vertical velocity (w), wind-speed (speed; sqrt(u*u+v*v)) and wind vectors (wind3).

Choose from the following (note the cross-section macro hres_xs.mv uses slightly different names for the parameters).

Back to the tutorial

Further reading

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