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• HRES : spectral T1279 (16km grid) highest resolution 10 day deterministic forecast.
• ENS :   spectral T639 (34km 31km grid) resolution ensemble forecast (50 members) is run for days 1-10 of the forecast, T319 (70km) is run for days 11-15.

In 2016, the ECMWF operational forecasts has been was upgraded compared to 2012 and consisted of:

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These exercises use a relatively large domain with high resolution data. Some of the plotting options can therefore require significant amounts of memory. If the virtual machine freezes when running metviewMetview, please try increasing the memory assigned to the VM.

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

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In this exercise, the development of Hurricane Nadine and the cut-off flow up to the 20th September 2012 is studied.

Begin by entering the folder labelled 'Analysis':

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This task will look at the synoptic development of Hurricane Nadine and the cutoff low up to 00Z, 20th September 2012. The forecasts in the next exercises start from this time and date.

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plot1=["pv320K"]

plot1=["z500.s","pv320K","mslp"]

Task 3: Changing the map geographical area

Task 3: Changing the map geographical area

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

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Task 1: Synoptic development (day 0-5)

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To add the forecast track of Hurricane Nadine drag and drop the mv_track.mv icon onto any map.

Precipitation over France

Precipitation over France

Choose a hres macro (hres_1x1 or hres_2x2) Choose a hres macro 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.

Other suggested isobaric maps

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Task 2: Vertical structure and forecast evolution to day 10

Task 2: Vertical structure and forecast evolution to day 10

This task focuses on This task focuses on the fate of Nadine and examines vertical PV cross-sections of Nadine and the cutoff at different forecast times to characterize the diabatic warm core PV tower of Nadine compared to the upper level PV cold core of the cutoff.

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

The plot shows the cross-section for the 22nd Sept., (day 2 of the forecast). The plot shows , 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 Sept). Nadine has now intensified as it approaches the coast.

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

Changing cross-section location

To change the forecast length for hres_1x1.mv and hres_2x2.mv, right-click, select Edit and change:

fclen=5

to

fclen=10

Changing cross-section location

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

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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 Sept), 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.

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Cyclone phase space (CPS) diagrams

An objectively defined cyclone phase space (CPS) is described using the storm-motion-relative thickness asymmetry (symmetric/non-frontal versus asymmetric/frontal) and vertical derivative of horizontal height gradient (cold- versus warm-core structure via the thermal wind relationship). A cyclone's life cycle can then be analyzed within this phase space, providing insight into the cyclone structural evolution.

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In this case study, there are two operational ensemble datasets, one from the original 2012 operational forecast, the other from a reforecast of the event using the 2016 operational ensemble.

An ensemble forecast consists of:

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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

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.

#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_to_an.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.

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.

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 exerciseThis 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

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

Task

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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.

#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"]

Spaghetti plots - another way to visualise spread

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_ref_spag.mv  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.

The red contour line shows the control forecast of the ensemble.

Note that this  may animate slowly because of the computations required.

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

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.

Precipitation over France

Use stamp.mv and plot total precipitation ('tp') over France (mapType=2) for 00Z 24-09-2012.

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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_an_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.

The red contour line shows the control forecast of the ensemble.

Note that this  may animate slowly because of the computations required.

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

Task 3: 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.

Precipitation over France

Use stamp.mv and plot total precipitation ('tp') over France (mapType=2) for 00Z 24-09-2012.

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 forecastforecasts.

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

Use the ens_to_anref_diff.mv (rename ens_to_hres_diff.mv and replace 'an' with 'hres')and icon to compare an ensemble member to the HRES forecast. Use pf_to_cf_diff.mvcan be used to compare ensemble members .

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TODO:

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to

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the control forecast.

param="mslp"
ensType="pf30"

ensType="cf"

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

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Sea-surface temperature

Compare the SST parameter used for the ens_ 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.

Cross-sections of

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an ensemble member

To show a cross-section of a particular ensemble member, use the macro ens_xs.mv.

This works in the same way as the hres_xs.mv macros.

Identifying sensitive regions

Find ensemble members that appear to produce a better forecast and look to see how the initial development in these members differs.

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Exercise 4. CDF, percentiles and probabilities

Recap

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_{Cumulative distribution function for a normal
distribution with varying standard deviation ( σ)}

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Exercise 4: CDF, percentiles and probabilities

Recap

Cumulative distribution function

The figures above illustrate the relationship between a normal distribution and its associated cumulative distribution function. The CDF is constructed from the area under the probability density function.

The CDF gives the probability that a value on the curve will be found to have a value less than or equal to the corresponding value on the x-axis. For example, in the figure, the probability for values less than or equal to X=0 is 50%.

The shape of the CDF curve is related to the shape of the normal distribution. The width of the CDF curve is directly related to the value of the standard deviation of the probability distribution function.

For an ensemble, the width is therefore related to the 'ensemble spread'. For a forecast ensemble where all values were the same, the CDF would be a vertical straight line.

Not all parameters will have a Gaussian distribution in values from the ensemble. This will be apparent in the exercises below.

Percentiles and probabilities

For a specified location, the CDF gives the probability that the parameter (for example, total precipitation) is below or equal to the percentile value, p, from the ensemble forecast. This means that the probability of the precipitation being above the value is 1-p.

A probability map then shows the spatial distribution of the precipitation exceeding a specific threshold, p, for example, a map showing the probability that the precipitation will exceed a threshold of 20mm in a 6hr period. A percentile map is very similar and shows the spatial distribution of the given percentile, for example, a map of total precipitation (in mm) for a percentile of 95%.

In the next tasks, we will look at probabilities of the total precipitation in different ways, highlighting the differences between the two forecast ensembles.

Task 1: Plot probabilities and percentiles of total precipitation

Image Added

Enter the folder Probabilities in the openifs_2018 folder.

Image Added

The prob_tp_compare.mv icon will produce maps over France showing the probability that total 6-hourly precipitation exceeds a threshold expressed in mm, for both the 2012 and 2016 forecast ensembles.

Maps are produced for 3 forecast times: +90, +96 and +102 hours.

Edit prob_tp_compare.mv and set the probability to 10mm:

#The probability of precipitation greater than (mm)
prob=10

Leave the location as an empty string for now:

location=""

Run the macro and view the map.

Location for CDF

Using the probability map, click the cursor data icon Image Added and move the pointer over the map for +96h and choose a location in the region of highest rainfall. Do this for both the 2012 and 2016 ensemble map.

Make a note of the latitude and longitude coordinates. The highest rainfall area was approximately over the Cévennes mountains ( 44°25′34″N 03°44′21″E ).

Edit prob_tp_compare.mv and set the location:

location=[44.0,4.1]   # [ lat, lon ] -- use your own values!

and replot the map. A small purple dot will appear at the location specified. If the dot is not in the right location, change it and replot.

Probabilities

Using the plotted probability map for 10mm precipitation threshold, use the cursor data icon to read the probability at the chosen location for +96 hours. Make a note of this value.

Edit prob_tp_compare.mv, and change the threshold value to 20mm:

prob=20

Replot the map and make a note of the probability at your chosen location.

Finally change the threshold probability to 30mm and replot:

prob=30

At your chosen location, using the cursor data icon, make a note of the probability for the 30mm threshold values.

You should now have the probability values that total precipitation will exceed 10mm, 20mm and 30mm, for both the 2012 and 2016 ensembles, for forecast time +96 hours for your chosen location.

Task 2: Plot the CDF

Image Added

Task 1:  Cumulative distribution function

The figures above illustrate the relationship between a normal distribution and its associated cumulative distribution function. The CDF is constructed from the area under the probability density function.

The CDF gives the probability that a value on the curve will be found to have a value less than or equal to the corresponding value on the x-axis. For example, in the figure, the probability for values less than or equal to X=0 is 50%.

The shape of the CDF curve is related to the shape of the normal distribution. The width of the CDF curve is directly related to the value of the standard deviation of the probability distribution function.

For an ensemble, the width is therefore related to the 'ensemble spread'.

For a forecast ensemble where all values were the same, the CDF would be a vertical straight line.

Plot the CDFs

Enter the folder 'XXXXXXXXX'

Image Removed

This exercise uses the cdf.mv icon.

Right-click, select 'Edit' and then plot a CDF for your location chosen in Task 1 for the 2012 ensemble forecast:

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param="tp"
station=[44.0,4.0]    # !use your own values!
expID="ens_oper"

Make sure the steps value is set correctly to +96 hours (00Z 24th Sept):

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

Note that only MSLP, 2m temperature (t2) and 10m wind-speed (speed10) are available for the CDF.

Make sure useClusters='off'.

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...

Do the same for the 2016 operational ensemble reforecast:

expID="ens_2016"

Compare the CDF from the different forecast ensembles.

Task 1. Plot percentiles of total precipitation

To further compare the 2012 and 2016 ensemble forecasts, plots showing the percentile amount and probabilities above a threshold can be made for total precipitation.

Use these icons:

Image Removed

Both these macros will use the 6-hourly total precipitation for forecast steps at 90, 96 and 102 hours, plotted over France.

Edit the percentile_tp_compare.mv icon.

Set the percentile for the total precipitation to 75%:

#The percentile of ENS precipitation forecast
perc=75

Run the macro and compare the percentiles from both the forecasts.  Change the percentiles to see how the forecasts differ.

Task 2: Plot probabilities of total precipitation

This macro will produce maps showing the probability of 6-hourly precipitation for the same area as in Task 1.

In this case, the maps show the probability that total precipitation exceeds a threshold expressed in mm.

Compare with probability map values

Using the CDF graph for the 2012 ensemble, read the probability that total precipitation will exceed 10mm. For example, see what percentile value, p,  is indicated on the y-axis for x=10mm. The probability that total precipitation exceeds this value is then 100-p.

The value read from the CDF graph in this way should agree with the value you obtained by reading the probability value from the map in Task 1.

Check your probabilities for 20mm and 30mm total precipitation.

The values may not match exactly as the number of samples (ensembles forecasts in this case) is limited.

Task 3. Plot percentiles of total precipitation

To further compare the 2012 and 2016 ensemble forecasts, plots showing the percentile amount above a threshold can be made for total precipitation.

These can also be compared to the CDF curves from Task 2.

Image Added

As before, this will use the 6-hourly total precipitation for forecast steps at 90, 96 and 102 hours, plotted over France.

Edit the percentile_tp_compare.mv icon.

Set the percentile for the total precipitation to 70% and specify the location as in Tasks 1 & 2Edit the prob_tp_compare.mv and set the probability to 20mm:

#The probabilitypercentile of ENS precipitation greater than
prob=20 forecast
perc=70

location=[44.0,4.1]   # [ lat, lon ] -- use your own values!

Plot the map. It is very similar to the probability map but now shows precipitation values (in mm) for the specified percentile.

From the CDF graph, read the percentile value of 70% on the y-axis and find the total precipitation value indicated on the x-axis.

Use the cursor data icon on the map, as before, and confirm the CDF value agrees with the value at the location on the map (shown by the purple dot).

Repeat this by setting the percentile to 80% and 95%Run the macro and view the map. Try changing the threshold value and run.

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Exercise 5: Cluster analysis

The paper by Pantillon et al, describes the use of clustering to identify the main scenarios among the ensemble members.This exercise repeats some of the plots from the previous one but this time with clustering enabled.

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

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• Construct your own qualitative clusters by choosing members for two clusters.
• Generate clusters using principal component analysis (similar to Pantillon et al).

Image Added

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

Task 1: Create your own clusters

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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 scenariosthe 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)

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You can create multiple cluster definitions by using the 'Duplicate' menu option to make copies of the file for use in the plotting macros..

The filename is important!
The first part of the name 'ens_oper' refers to the ensemble dataset and must match the name used in the plotting macro. 
The 'example' part of the filename can be changed to your choice and should match the 'clustersId'.
As an example a filename of: ens_both_cluster.fred.txt would require 'expId=ens_both', 'clustersId=fred' in the macro.

Plot

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maps of parameters as clusters

Image Added

The macro cluster_to_ref.mv can be used to plot maps of parameters as clusters and compared to the ensemble control forecast and the HRES forecast.

Use cluster_to_ref.mv to plot z500 maps of your two clusters.

If your cluster definition file is called '

Use the clusters of ensemble members you have created in ens_oper_cluster.example.txt', then Edit cluster_to_ref.

Set clustersId='example' in each of the ensemble plotting macros to enable cluster highlighting.

Replot ensembles:

Stamp maps: the stamp maps will be reordered such at the ensemble members will be groups according to their cluster. Applies to stamp.mv icon. This will make it easier to see the forecast scenarios according to your clustering.

Spaghetti maps: with clusters enabled, two additional maps are produced which show the contour lines for each cluster.

Plot maps of parameters as clusters

The macro cluster_to_an.mv can be used to plot maps of parameters as clusters and compared to the HRES forecasts. TODO: rename cluster_to_an.mv to cluster_hres.mv and comment out plot of AN.

Use cluster_to_an.mv to plot z500 maps of your two clusters.

If your cluster definition file is called 'ens_oper_cluster.example.txt', then Edit cluster_to_an.mv and set:

#ENS members (use ["all"] or a list of members like [1,2,3]
members_1=["cl.example.1"]
members_2=["cl.example.2"]

If your cluster definition file is has another name, e.g. ens_oper_cluster.fred.txt, then members_1=["cl.fred.1"].

Plot other parameters:

Plot total precipitation for France (mapType=2). Compare with Figure 8. in Pantillon et al.

mv and set:

#ENS members (use ["all"] or a list of members like [1,2,3]
members_1=["cl.example.1"]
members_2=["cl.example.2"]

If your cluster definition file has another name, e.g. ens_oper_cluster.fred.txt, then members_1=["cl.fred.1"].

Plot ensembles with clusters

In this part of the task, redo the plots from the previous exercise which looked at ways of plotting ensemble data, but this time with clustering enabled.

Use the clusters of ensemble members you have created in ens_oper_cluster.example.txt.

Set clustersId='example' in each of the stamp.mv and ens_to_ref_spag.mv to enable cluster highlighting.

If time, also try the ens_part_to_all.mv icon. This compares the spread and mean of part of the ensemble to the full ensemble.

Plot other parameters

Use the stamp.mv icon and change it to plot the total precipitation over France with clusters enabled.e.g.

param="tp"
expId="ens_oper"
mapType=2
clustersId="example"

If you choice of clustering is accurate, you should see a clear separation of precipitation over France between the two clusters.

Task 2: Empirical orthogonal functions / Principal component analysis

A quantitative way of clustering an ensemble is by computing uses empirical orthogonal functions from the differences between the ensemble members and the control forecast. and then using an algorithm to determine the clusters from each ensemble as projected in EOF space (mathematically).

As a smooth dynamical field, Although geopotential height at 500hPa at 00 00Z 24/9/2012 is recommend (it used in the paper by Pantillon et al.), but the steps described below can be used for any parameter at any step.

Image Removed

Image Added

The eof.mv macro computes the EOFs and the clustering.

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param="z500"
expId="ens_oper"
steps=[2012-09-24 00:00]

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ens_oper_cluster.eof.txt

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#ENS members (use ["all"] or a list of members like [1,2,3]
members_1=["cl.eof.1"]
members_2=["cl.eof.2"]

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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?

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Is geopotential the best parameter to use?

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Changing the number of clusters

To change the number of clusters created by the EOF analysis,

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edit eof.mv.

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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_

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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 6. Assessment of forecast errors

Exercise 6. 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 In this exercise, various methods for presenting the forecast error are presentedused in the tasks below.  The clusters created in the previous exercise above can also be used.

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TODO:

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

Image Added

Task 1: Satellite images

Open the folder 'satellite'folder 'satellite' (scroll the window if it is not visible).

This folder contains satellite images (water vapour, infra-red, false colour) for 00Z on 20-09-2012 and animations of the infra-red and water vapour images.

Double click the images to display them and watch the observed behaviour of Nadine and the cut-off low.

Task 2: Analyses from 20th Sept.

The first task is to now look at the analyses from 20th Sept to observe what actually happened.

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TODO

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.

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Task 2: Analyses from 20th Sept.

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

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Enter the folder 'Forecast_errors'  (TODO: include icon graphic)

As in Exercise 1, task 1. 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 plotthe 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 3: Compare forecast to analysis

Plot forecast difference maps to see how and when the forecast differed from the analyses.

Use the hres_to_an_diff.mv icon and plot the differences between the z500, MSLP and other fields to how the forecast differences evolve.

Also try the ctrl_to_an_diff.mv icon which plots the difference but this time using the ensemble control forecast.

Task 4: Forecast error curve

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Image Removed

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Task 4: Forecast error curve

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.

Root-mean square error curves are a standard measure to determine forecast error compared to the analysis and several of the exercises will use them. The RMSE is computed by taking the square-root of the mean of the forecast difference between the HRES and analyses. RMSE of the 500hPa geopotential is a standard measure for assessing forecast model performance at ECMWF (for more information see: http://www.ecmwf.int/en/forecasts/quality-our-forecasts).

Right-click the hres_rmse.mv icon, select 'Edit' and plot the RMSE curve for z500.

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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.

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Appendix

Further reading

For more information on the stochastic physics scheme in IFS, see the article:

Shutts et al, 2011, ECMWF Newsletter 129.

Acknowledgements

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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